2000 character limit reached
How do transportation professionals perceive the impacts of AI applications in transportation? A latent class cluster analysis (2401.08915v1)
Published 17 Jan 2024 in stat.AP and cs.CY
Abstract: Recent years have witnessed an increasing number of AI applications in transportation. As a new and emerging technology, AI's potential to advance transportation goals and the full extent of its impacts on the transportation sector is not yet well understood. As the transportation community explores these topics, it is critical to understand how transportation professionals, the driving force behind AI Transportation applications, perceive AI's potential efficiency and equity impacts. Toward this goal, we surveyed transportation professionals in the United States and collected a total of 354 responses. Based on the survey responses, we conducted both descriptive analysis and latent class cluster analysis (LCCA). The former provides an overview of prevalent attitudes among transportation professionals, while the latter allows the identification of distinct segments based on their latent attitudes toward AI. We find widespread optimism regarding AI's potential to improve many aspects of transportation (e.g., efficiency, cost reduction, and traveler experience); however, responses are mixed regarding AI's potential to advance equity. Moreover, many respondents are concerned that AI ethics are not well understood in the transportation community and that AI use in transportation could exaggerate existing inequalities. Through LCCA, we have identified four latent segments: AI Neutral, AI Optimist, AI Pessimist, and AI Skeptic. The latent class membership is significantly associated with respondents' age, education level, and AI knowledge level. Overall, the study results shed light on the extent to which the transportation community as a whole is ready to leverage AI systems to transform current practices and inform targeted education to improve the understanding of AI among transportation professionals.
- Vasudevan, M., Townsend, H., Schweikert, E., Wunderlich, K., Burnier, C., Hammit, B., Gettman, D., Ozbay, K.: Real-world ai scenarios in transportation for possible deployment. Technical report (2020) Van Noorden and Perkel [2023] Van Noorden, R., Perkel, J.M.: Ai and science: what 1,600 researchers think. Nature 621(7980), 672–675 (2023) Gross [2022] Gross, A.: Consumer skepticism toward autonomous driving features justified. Sept 9, 2022 (2022) Walker [2020] Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Van Noorden, R., Perkel, J.M.: Ai and science: what 1,600 researchers think. Nature 621(7980), 672–675 (2023) Gross [2022] Gross, A.: Consumer skepticism toward autonomous driving features justified. Sept 9, 2022 (2022) Walker [2020] Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Gross, A.: Consumer skepticism toward autonomous driving features justified. Sept 9, 2022 (2022) Walker [2020] Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Van Noorden, R., Perkel, J.M.: Ai and science: what 1,600 researchers think. Nature 621(7980), 672–675 (2023) Gross [2022] Gross, A.: Consumer skepticism toward autonomous driving features justified. Sept 9, 2022 (2022) Walker [2020] Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Gross, A.: Consumer skepticism toward autonomous driving features justified. Sept 9, 2022 (2022) Walker [2020] Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Gross, A.: Consumer skepticism toward autonomous driving features justified. Sept 9, 2022 (2022) Walker [2020] Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Walker, J.: Artificial intelligence (AI) for intelligent transportation systems (ITS) program. Technical report, U.S. Department of Transportation (2020) Jiang [2022] Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Jiang, W.: Cellular traffic prediction with machine learning: A survey. Expert Systems with Applications 201 (2022) https://doi.org/10.1016/j.eswa.2022.117163 . Accessed 2023-07-26 Derrow-Pinion et al. [2021] Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Derrow-Pinion, A., She, J., Wong, D., Lange, O., Hester, T., Perez, L., Nunkesser, M., Lee, S., Guo, X., Wiltshire, B., Battaglia, P.W., Gupta, V., Li, A., Xu, Z., Sanchez-Gonzalez, A., Li, Y., Velickovic, P.: ETA prediction with graph neural networks in Google Maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, Virtual Event Queensland Australia (2021). https://doi.org/10.1145/3459637.3481916 . https://dl.acm.org/doi/10.1145/3459637.3481916 Accessed 2023-07-26 Tien [2022] Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Tien, A.: The roles of artificial intelligence (AI) and machine learning (ML) in an info-centric national airspace system (NAS). (2022) Müller-Hannemann et al. [2022] Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Müller-Hannemann, M., Rückert, R., Schiewe, A., Schöbel, A.: Estimating the robustness of public transport schedules using machine learning. Transportation Research Part C: Emerging Technologies 137 (2022) https://doi.org/10.1016/j.trc.2022.103566 . Accessed 2023-07-26 Ge and Yuanzhi Jin [2021] Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Ge, X., Yuanzhi Jin: Artificial intelligence algorithms for proactive dynamic vehicle routing problem. Applications of Artificial Intelligence in Process Systems Engineering, 497–522 (2021) Tsai [2023] Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Tsai, Y.: Successful AI Applications for curve safety assessment & compliance, and pavement asset management. (2023) Lv et al. [2021] Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Lv, Z., Lou, R., Singh, A.K.: AI empowered communication systems for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems 22(7) (2021) https://doi.org/10.1109/TITS.2020.3017183 . Accessed 2023-07-26 Iyer [2021] Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Iyer, L.S.: AI enabled applications towards intelligent transportation. Transportation Engineering 5 (2021) https://doi.org/10.1016/j.treng.2021.100083 . Accessed 2023-07-26 Nikitas et al. [2020] Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Nikitas, A., Michalakopoulou, K., Njoya, E.T., Karampatzakis, D.: Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era. Sustainability 12(7) (2020) https://doi.org/10.3390/su12072789 . Accessed 2023-07-26 Abduljabbar et al. [2019] Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.A.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1) (2019) https://doi.org/10.3390/su11010189 . Accessed 2023-07-26 Okrepilov et al. [2022] Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Okrepilov, V.V., Kovalenko, B.B., Getmanova, G.V., Turovskaj, M.S.: Modern trends in artificial intelligence in the transport system. Transportation Research Procedia 61 (2022) https://doi.org/10.1016/j.trpro.2022.01.038 . Accessed 2023-07-26 Hasan et al. [2019] Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Hasan, U., Whyte, A., Al Jassmi, H.: A review of the transformation of road transport systems: are we ready for the next step in artificially intelligent sustainable transport? Applied System Innovation 3(1) (2019) https://doi.org/10.3390/asi3010001 . Accessed 2023-07-26 Rigole [2014] Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Rigole, P.-J.: Study of a shared autonomous vehicles based mobility solution in Stockholm. Master of Science Thesis, Royal Institute of Technology, Stockholm, Sweden (2014). http://bit.ly/1Qig7Cu Kouziokas [2017] Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Kouziokas, G.N.: The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia 24 (2017) https://doi.org/10.1016/j.trpro.2017.05.083 . Accessed 2023-07-26 Boukerche et al. [2020] Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Boukerche, A., Tao, Y., Sun, P.: Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks 182 (2020) https://doi.org/10.1016/j.comnet.2020.107484 . Accessed 2023-07-26 Yang et al. [2022] Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Yang, H.F., Ling, Y., Kopca, C., Ricord, S., Wang, Y.: Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence. Transportation Research Part C: Emerging Technologies 145 (2022) https://doi.org/10.1016/j.trc.2022.103896 . Accessed 2023-07-26 Ai and Tsai [2016] Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Ai, C., Tsai, Y.J.: Automated sidewalk assessment method for Americans with disabilities act compliance using three-dimensional mobile lidar. Transportation Research Record: Journal of the Transportation Research Board 2542(1) (2016) https://doi.org/10.3141/2542-04 . Accessed 2023-07-26 Boldini et al. [2021] Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Boldini, A., Garcia, A.L., Sorrentino, M., Beheshti, M., Ogedegbe, O., Fang, Y., Porfiri, M., Rizzo, J.-R.: An inconspicuous, integrated electronic travel aid for visual impairment. ASME Letters in Dynamic Systems and Control 1(4) (2021) https://doi.org/10.1115/1.4050186 . Accessed 2023-07-26 Yin et al. [2020] Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Yin, M., Li, K., Cheng, X.: A review on artificial intelligence in high-speed rail. Transportation Safety and Environment 2(4) (2020) https://doi.org/10.1093/tse/tdaa022 . Accessed 2023-07-26 Adler and Blue [1998] Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Adler, J.L., Blue, V.J.: Toward the design of intelligent traveler information systems. Transportation Research Part C: Emerging Technologies 6(3) (1998) https://doi.org/10.1016/S0968-090X(98)00012-6 . Accessed 2023-07-26 Tsaih and Hsu [2018] Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Tsaih, R.-H., Hsu, C.: Artificial intelligence in smart tourism: a conceptual framework. ICEB 2018 Proceedings (Guilin, China) (2018) Sanchez et al. [2003] Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Sanchez, T.W., Stolz, R., Ma, J.S.: Moving to equity: addressing inequitable effects of transportation policies on minorities (2003). Accessed 2023-07-26 Najibi [2020] Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Najibi, A.: Racial discrimination in face recognition technology. Science in the News 24 (2020) Othman [2021] Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Othman, K.: Public acceptance and perception of autonomous vehicles: a comprehensive review. AI and Ethics 1(3), 355–387 (2021) Chen et al. [2020] Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Chen, S.-Y., Kuo, H.-Y., Lee, C.: Preparing society for automated vehicles: Perceptions of the importance and urgency of emerging issues of governance, regulations, and wider impacts. Sustainability 12(19), 7844 (2020) Butler et al. [2021] Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Butler, L., Yigitcanlar, T., Paz, A.: Factors influencing public awareness of autonomous vehicles: Empirical evidence from brisbane. Transportation research part F: traffic psychology and behaviour 82, 256–267 (2021) Hilgarter and Granig [2020] Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Hilgarter, K., Granig, P.: Public perception of autonomous vehicles: A qualitative study based on interviews after riding an autonomous shuttle. Transportation research part F: traffic psychology and behaviour 72, 226–243 (2020) Kim et al. [2019] Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Kim, S.H., Circella, G., Mokhtarian, P.L.: Identifying latent mode-use propensity segments in an all-AV era. Transportation Research Part A: Policy and Practice 130 (2019) https://doi.org/10.1016/j.tra.2019.09.015 . Accessed 2023-07-26 Dai et al. [2023] Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Dai, J., Wang, X.C., Ma, W., Li, R.: Future transport vision propensity segments: A latent class analysis of autonomous taxi market. Transportation Research Part A: Policy and Practice 173 (2023) https://doi.org/10.1016/j.tra.2023.103699 . Accessed 2023-07-26 Ton et al. [2020] Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Ton, D., Zomer, L.-B., Schneider, F., Hoogendoorn-Lanser, S., Duives, D., Cats, O., Hoogendoorn, S.: Latent classes of daily mobility patterns: the relationship with attitudes towards modes. Transportation 47, 1843–1866 (2020) Wang and Shen [2022] Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Wang, Y., Shen, Q.: A latent class analysis to understand riders’ adoption of on-demand mobility services as a complement to transit. Transportation, 1–19 (2022) Lee et al. [2020] Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Lee, Y., Circella, G., Mokhtarian, P.L., Guhathakurta, S.: Are millennials more multimodal? a latent-class cluster analysis with attitudes and preferences among millennial and generation x commuters in california. Transportation 47, 2505–2528 (2020) Horowitz and Kahn [2021] Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Horowitz, M.C., Kahn, L.: What influences attitudes about artificial intelligence adoption: Evidence from us local officials. Plos one 16(10), 0257732 (2021) Kassens-Noor et al. [2020] Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Kassens-Noor, E., Kotval-Karamchandani, Z., Cai, M.: Willingness to ride and perceptions of autonomous public transit. Transportation Research Part A: Policy and Practice 138, 92–104 (2020) Hohenberger et al. [2016] Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016) Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Hohenberger, C., Spörrle, M., Welpe, I.M.: How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice 94, 374–385 (2016)
- Yiheng Qian (1 paper)
- Tejaswi Polimetla (2 papers)
- Thomas W. Sanchez (1 paper)
- Xiang Yan (35 papers)