Towards spatiotemporal integration of bus transit with data-driven approaches (2402.17866v1)
Abstract: This study aims to propose an approach for spatiotemporal integration of bus transit, which enables users to change bus lines by paying a single fare. This could increase bus transit efficiency and, consequently, help to make this mode of transportation more attractive. Usually, this strategy is allowed for a few hours in a non-restricted area; thus, certain walking distance areas behave like "virtual terminals." For that, two data-driven algorithms are proposed in this work. First, a new algorithm for detecting itineraries based on bus GPS data and the bus stop location. The proposed algorithm's results show that 90% of the database detected valid itineraries by excluding invalid markings and adding times at missing bus stops through temporal interpolation. Second, this study proposes a bus stop clustering algorithm to define suitable areas for these virtual terminals where it would be possible to make bus transfers outside the physical terminals. Using real-world origin-destination trips, the bus network, including clusters, can reduce traveled distances by up to 50%, making twice as many connections on average.
- Analysis of public bus transportation of a Brazilian city based on the theory of complex networks using the p-space. Mathematical Problems in Engineering, 2016. DOI: 10.1155/2016/3898762.
- Algoritmo para detecção de itinerários do transporte público usando dados de gps dos Ônibus. In Anais do VII Workshop de Computação Urbana, pages 1–14, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/courb.2023.739.
- Graph mining for the detection of overcrowding and waste of resources in public transport. Journal of Internet Services and Applications, 9:22. DOI: 10.1186/s13174-018-0094-3.
- GPS data analytics for the assessment of public city bus transportation service quality in Bangkok. Sustainability, 15(7). DOI: 10.3390/su15075618.
- Temporal performance analysis of bus transportation using link streams. Mathematical Problems in Engineering, 2019. DOI: 10.1155/2019/6139379.
- Smart bus fleet management system using IoT. In 2022 Fourth International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pages 01–06. DOI: 10.1109/ICERECT56837.2022.10059646.
- Anatomy and efficiency of urban multimodal mobility. Scientific Reports, 4:6911. DOI: 10.1038/srep06911.
- The multilayer temporal network of public transport in Great Britain. Scientific Data, 2:140056. DOI: 10.1038/sdata.2014.56.
- On strategies to help reduce contamination on public transit: a multilayer network approach. Applied Network Science, 8(1):1–22. DOI: 10.1007/s41109-023-00562-7.
- IoT bus monitoring system via mobile application. In 2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), pages 125–130. DOI: 10.1109/I2CACIS54679.2022.9815268.
- IPPUC (2017). Consolidação de Dados de Oferta, Demanda, Sistema Viário e Zoneamento: Relatório 5 - Pesquisa Origem-Destino Domiciliar. URL: http://admsite2013.ippuc.org.br/arquivos/documentos/D536/D536_002_BR.pdf (Last accessed: 2023-06-14).
- Lawhead, J. (2015). Learning geospatial analysis with Python. Packt Publishing Ltd, Birmingham, 2nd edition.
- Spatiotemporally complementary effect of high-speed rail network on robustness of aviation network. Transportation Research Part A: Policy and Practice, 155:95–114. DOI: https://doi.org/10.1016/j.tra.2021.10.020.
- Multi-attention graph neural networks for city-wide bus travel time estimation using limited data. Expert Systems with Applications, 202:117057. DOI: 10.1016/j.eswa.2022.117057.
- Bus travel time prediction with real-time traffic information. Transportation Research Part C: Emerging Technologies, 105:536–549. DOI: 10.1016/j.trc.2019.06.008.
- Transit performance assessment based on graph analytics. Transportmetrica A: Transport Science, 15(2):1382–1401. DOI: 10.1080/23249935.2019.1596991.
- Map matching: Uma análise de dados streaming de trajetórias de GPS no transporte público. In Temas Emergentes: Cidades Inteligentes (XVIII SBSI), pages 294–301. SBC. DOI: 10.5753/sbsi_estendido.2022.221647.
- Crisis of public transport by bus in developing countries: a case study from brazil. International Journal of Sustainable Development and Planning, 8:348–361. DOI: 10.2495/SDP-V8-N3-348-361.
- Performance improvement for metro passenger flow forecast using spatio-temporal deep neural network. Neural Computing and Applications, pages 1–12. DOI: 10.1007/s00521-021-06522-5.
- Panigrahi, N. (2014). Computing in geographic information systems. CRC Press, Boca Raton, Florida, 1st edition.
- Assessing public transit performance using real-time data: spatiotemporal patterns of bus operation delays in columbus, ohio, usa. International Journal of Geographical Information Science, 34:367–392. DOI: 10.1080/13658816.2019.1608997.
- Plataforma computacional para construção de um banco de dados de grafo do sistema de transporte de Curitiba. In IV Workshop de Computação Urbana, pages 125–137. SBC. DOI: 10.5753/courb.2020.12358.
- Conformity analysis of GTFS routes and bus trajectories. In XXXIV Simpósio Brasileiro de Banco de Dados, pages 199–204. SBC. DOI: 10.5753/sbbd.2019.8823.
- SMAFramework: Urban Data Integration Framework for Mobility Analysis in Smart Cities. In Proceedings of the 20th ACM International Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM ’17, page 227–236, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3127540.3127569.
- Advances on Urban Mobility Using Innovative Data-Driven Models, pages 1–38. Springer International Publishing, Cham. DOI: 10.1007/978-3-030-15145-4_57-1.
- Review and evaluation of methods in transport mode detection based on GPS tracking data. Journal of Traffic and Transportation Engineering (English Edition), 8(4):467–482. DOI: 10.1016/j.jtte.2021.04.004.
- Characterization of public transit mobility patterns of different economic classes. Sustainability, 12(22). DOI: 10.3390/su12229603.
- GPS based bus tracking system. In 2015 International Conference on Computer, Communication and Control (IC4), pages 1–6. DOI: 10.1109/IC4.2015.7375712.
- Smart bus tracking and management system using IoT. Asian Journal of Applied Science and Technology (AJAST), 1(2). Available at SSRN: https://ssrn.com/abstract=2941150.
- URBS (2022a). Características da rede integrada de transporte. URL: https://www.urbs.curitiba.pr.gov.br/transporte/rede-integrada-de-transporte (Last accessed: 2023-03-27).
- URBS (2022b). Web-service: Dados públicos da rede integrada do transporte coletivo de Curitiba. URL: https://www.curitiba.pr.gov.br/dadosabertos/busca/?grupo=8 (Last accessed: 2023-03-27).
- Urban mobility challenges–an exploratory analysis of public transportation data in Curitiba. Revista de Informática Aplicada, 12(1). Available at RIA: https://seer.uscs.edu.br/index.php/revista_informatica_aplicada/article/view/6905/2996.
- Review on application based bus tracking system. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), pages 876–880. DOI: 10.1109/IC3I56241.2022.10072449.
- A multilayer and time-varying structural analysis of the Brazilian air transportation network. In Latin America Data Science Workshop, volume 2170 of CEUR Workshop Proceedings, pages 57–64. Available at LADaS: https://ceur-ws.org/Vol-2170/paper8.pdf.
- Big data in public transportation: a review of sources and methods. Transport Reviews, 39(6):795–818. DOI: 10.1080/01441647.2019.1616849.
- Yen, J. Y. (1971). Finding the k shortest loopless paths in a network. Management Science, 17:712–716. DOI: 10.1287/mnsc.17.11.712.
- Detecting illegal pickups of intercity buses from their GPS traces. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 2162–2167. DOI: 10.1109/ITSC.2014.6958023.
- Policy zoning for efficient land utilization based on spatio-temporal integration between the bicycle-sharing service and the metro transit. Sustainability, 13(1):141. DOI: 10.3390/su13010141.
- Analysis of spatial-temporal characteristics of operations in public transport networks based on multisource data. Journal of Advanced Transportation, 2021:1–15. DOI: 10.1155/2021/6937228.
- Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus. International Journal of Geographical Information Science, pages 1–27. DOI: 10.1080/13658816.2023.2203218.