Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
157 tokens/sec
GPT-4o
43 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

An Adaptive System Architecture for Multimodal Intelligent Transportation Systems (2402.08817v1)

Published 13 Feb 2024 in eess.SY and cs.SY

Abstract: Multimodal intelligent transportation systems (M-ITS) encompass a range of transportation services that utilise various modes of transport and incorporate intelligent technologies for enhanced efficiency and user experience. There are several challenges in M-ITS including data integration, Interoperability, scalability, user experience, etc. To address these challenges, such a system requires an adaptive system architecture that enables M-ITS to operate as an integrated ecosystem. In this paper, we provide an adaptive, user-centric, and layered architecture for multimodal transportation systems. The proposed architecture ensures scalability for seamless interactions of various subcomponents, that are often managed by different stakeholders. Concurrently, the data architecture is detailed, covering diverse data sources, advanced analytics, and stringent governance, providing a robust basis for intelligent decision-making. We provide two example use cases of the proposed architecture, showing how the data architecture and the system architecture can be fused and serve multimodal intelligent transport services.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (20)
  1. F. Golpayegani and et al., “Intelligent shared mobility systems: A survey on whole system design requirements, challenges and future direction,” IEEE Access, vol. 10, pp. 35302–35320, 2022.
  2. A. W. ter Mors, J. M. Valk, and C. Witteveen, “Task coordination and decomposition in multi-actor planning systems,” in Proceedings of the Workshop on Software-Agents in Information Systems and Industrial Applications (SAISIA), pp. 83–94, Frankfurter IRB Verlag, 2006.
  3. P. Agbaje, A. Anjum, A. Mitra, E. Oseghale, G. Bloom, and H. Olufowobi, “Survey of interoperability challenges in the internet of vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 22838–22861, 2022.
  4. J. Rak, R. Girao-Silva, T. Gomes, G. Ellinas, B. Kantarci, and M. Tornatore, “Disaster resilience of optical networks: State of the art, challenges, and opportunities,” Optical Switching and Networking, vol. 42, p. 100619, 2021.
  5. S. Gringeri, B. Basch, V. Shukla, R. Egorov, and T. J. Xia, “Flexible architectures for optical transport nodes and networks,” IEEE Communications Magazine, vol. 48, no. 7, pp. 40–50, 2010.
  6. K. F. Ystgaard, L. Atzori, D. Palma, P. E. Heegaard, L. E. Bertheussen, M. R. Jensen, and K. De Moor, “Review of the theory, principles, and design requirements of human-centric internet of things (iot),” Journal of Ambient Intelligence and Humanized Computing, vol. 14, no. 3, pp. 2827–2859, 2023.
  7. M. C. Hamilton, S. A. Thekdi, E. M. Jenicek, R. S. Harmon, M. E. Goodsite, M. P. Case, C. W. Karvetski, and J. H. Lambert, “Case studies of scenario analysis for adaptive management of natural resource and infrastructure systems,” Environment Systems & Decisions, vol. 33, pp. 89–103, 2013.
  8. Y. Guan, A. M. Annaswamy, and H. E. Tseng, “Towards dynamic pricing for shared mobility on demand using markov decision processes and dynamic programming,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–7, IEEE, 2020.
  9. S. Saeed, S. A. Suayyid, M. S. Al-Ghamdi, H. Al-Muhaisen, and A. M. Almuhaideb, “A systematic literature review on cyber threat intelligence for organizational cybersecurity resilience,” Sensors, vol. 23, no. 16, p. 7273, 2023.
  10. J. R. R. García and et al., “State of the art of electric mobility as a service (emaas): an overview of ecosystems and system architectures,” in 32nd International Electric Vehicle Symposium 2019: A world of E-motion, 2019.
  11. Q.-V. Pham, F. Fang, V. N. Ha, M. J. Piran, M. Le, L. B. Le, W.-J. Hwang, and Z. Ding, “A survey of multi-access edge computing in 5g and beyond: Fundamentals, technology integration, and state-of-the-art,” IEEE access, vol. 8, pp. 116974–117017, 2020.
  12. S. Fiore, D. Elia, C. E. Pires, D. G. Mestre, C. Cappiello, M. Vitali, N. Andrade, T. Braz, D. Lezzi, R. Moraes, et al., “An integrated big and fast data analytics platform for smart urban transportation management,” IEEE Access, vol. 7, pp. 117652–117677, 2019.
  13. A. Staniewska, I. Sykta, A. Ozimek, K. Barnaś, M. Dudek, M. Marasik, and K. Racoń-Leja, “Framework for the design of a small transport hub as an interdisciplinary challenge to implement sustainable solutions,” Sustainability, vol. 15, no. 14, p. 10975, 2023.
  14. K. Turoń, A. Kubik, F. Chen, H. Wang, and B. Łazarz, “A holistic approach to electric shared mobility systems development—modelling and optimization aspects,” Energies, vol. 13, no. 21, p. 5810, 2020.
  15. J. Dąbrowski, “Towards an adaptive framework for goal-oriented strategic decision-making,” in 2017 IEEE 25th International Requirements Engineering Conference (RE), pp. 538–543, IEEE, 2017.
  16. D. O. Rodrigues, G. Maia, T. Braun, A. A. Loureiro, M. L. Peixoto, and L. A. Villas, “Exploring hybrid-multimodal routing to improve user experience in urban trips,” Applied Sciences, vol. 11, no. 10, p. 4523, 2021.
  17. “DATEX II.” https://datex2.eu/.
  18. “SIRI (Service Interface for Real-time Information).” https://en.wikipedia.org/wiki/Service_Interface_for_Real_Time_Information.
  19. “TMDD (Traffic Management Data Dictionary).” https://www.tmdd.org/.
  20. “Cen/ts 17123.” https://www.cen.eu/work/areas/transport/Pages/default.aspx.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now