Abstract
Public transportation systems play a vital role for society, but they often fall short in addressing the dynamic needs of commuters. Intelligent Public Transportation Systems (IPTS) hold promise for enhancing efficiency and adapting to these evolving requirements. Digital twins (DT), virtual representations of real-world systems, can be leveraged to create dynamic replicas that guide real-time decision-making and optimization for IPTS. This paper examines the concept of digital twins and their potential for IPTS, highlighting the challenges and opportunities that must be addressed to fully capitalize on this technology. Moreover, a DT-based IPTS architecture is proposed leveraging on FIWARE Smart Data Models for data interoperability. Finally, a small real-world instance of the proposed architecture and data model is illustrated involving a bus-based IPTS where the DT technology is adopted to enable bus passenger demand prediction and bus scheduling update.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
Please note that JSON-LD @context is different from the concept of context-aware data system. Context can be defined as “any information that can be used to characterize the situation of an entity” [18], i.e., the state in which an entity is at a given time. Instead, the @context is used to define short-hand names that are part of JSON-LD payloads.
- 13.
- 14.
- 15.
- 16.
- 17.
Available at https://github.com/smart-data-models/dataModel.UrbanMobility, the data model has been adopted in the SynchroniCity European project https://cordis.europa.eu/project/id/732240.
- 18.
General Transit Feed Specification is an open standard used by public transport agencies to publish their transit data in a format that can be consumed by a wide variety of software applications.
- 19.
- 20.
- 21.
- 22.
- 23.
- 24.
- 25.
- 26.
- 27.
- 28.
- 29.
- 30.
- 31.
- 32.
- 33.
- 34.
- 35.
- 36.
- 37.
- 38.
- 39.
References
Amato, F., Di Martino, S., Mazzocca, N., Nardone, D., Rocco di Torrepadula, F., Sannino, P.: Bus passenger load prediction: challenges from an industrial experience. In: Karimipour, F., Storandt, S. (eds.) W2GIS 2022. LNCS, vol. 13238, pp. 93–107. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06245-2_9
Bao, L., Wang, Q., Jiang, Y.: Review of digital twin for intelligent transportation system. In: 2021 International Conference on Information Control, Electrical Engineering and Rail Transit (ICEERT), pp. 309–315 (2021). https://doi.org/10.1109/ICEERT53919.2021.00064
Bin, Y., Zhongzhen, Y., Baozhen, Y.: Bus arrival time prediction using support vector machines. J. Intell. Transp. Syst. 10(4), 151–158 (2006)
Cirillo, F., Solmaz, G., Berz, E.L., Bauer, M., Cheng, B., Kovacs, E.: A standard-based open source IoT platform: FIWARE. IEEE Internet Things Mag. 2(3), 12–18 (2019). https://doi.org/10.1109/IOTM.0001.1800022
Conde, J., Munoz, J., Alonso, A., Lòpez-Pernas, S., Salvachua, J.: Modeling digital twin data and architecture: a building guide with FIWARE as enabling technology. IEEE Internet Comput. 26, 7–14 (2021). https://doi.org/10.1109/MIC.2021.3056923
Conde, J., Munoz-Arcentales, A., Alonso, A., Huecas, G., Salvachùa, J.: Collaboration of digital twins through linked open data: architecture with FIWARE as enabling technology. IT Prof. 24(6), 41–46 (2022). https://doi.org/10.1109/MITP.2022.3224826
Dasgupta, S., Rahman, M., Lidbe, A.D., Lu, W., Jones, S.L.: A transportation digital-twin approach for adaptive traffic control systems. CoRR abs/2109.10863 (2021). https://arxiv.org/abs/2109.10863
Gavalas, D., et al.: Smart cities: recent trends, methodologies, and applications (2017)
Ghariani, N., Elkosantini, S., Darmoul, S., Ben Said, L.: A survey of simulation platforms for the assessment of public transport control systems. In: 2014 International Conference on Advanced Logistics and Transport (ICALT), pp. 85–90 (2014). https://doi.org/10.1109/ICAdLT.2014.6864088
Jafari, M., Kavousi-Fard, A., Chen, T., Karimi, M.: A review on digital twin technology in smart grid, transportation system and smart city: challenges and future. IEEE Access 11, 17471–17484 (2023). https://doi.org/10.1109/ACCESS.2023.3241588
Jenelius, E.: Data-driven bus crowding prediction based on real-time passenger counts and vehicle locations. In: 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MTITS2019) (2019)
Kale, A.: Collaboration of automotive, connected solutions and energy technologies for sustainable public transportation for Indian cities. In: 2019 IEEE Transportation Electrification Conference (ITEC-India), pp. 1–6. IEEE (2019)
Kim, K.M., Hong, S.P., Ko, S.J., Kim, D.: Does crowding affect the path choice of metro passengers? Transp. Res. Part A Policy Pract. 77, 292–304 (2015)
Kirimtat, A., Krejcar, O., Kertesz, A., Tasgetiren, M.F.: Future trends and current state of smart city concepts: a survey. IEEE Access 8, 86448–86467 (2020)
Kui, K., Schumann, R., Ivanjko, E.: A digital twin in transportation: real-time synergy of traffic data streams and simulation for virtualizing motorway dynamics. Adv. Eng. Inform. 55, 101858 (2023). https://doi.org/10.1016/j.aei.2022.101858. https://www.sciencedirect.com/science/article/pii/S1474034622003160
Martínez, R., Pastor, J.A., Àlvarez, B., Iborra, A.: A testbed to evaluate the FIWARE-based IoT platform in the domain of precision agriculture. Sensors 16(11) (2016). https://www.mdpi.com/1424-8220/16/11/1979
Megalingam, R.K., Raj, N., Soman, A.L., Prakash, L., Satheesh, N., Vijay, D.: Smart, public buses information system. In: 2014 International Conference on Communication and Signal Processing, pp. 1343–1347 (2014). https://doi.org/10.1109/ICCSP.2014.6950068
Munoz-Arcentales, A., López-Pernas, S., Conde, J., Alonso, l., Salvachúa, J., Hierro, J.J.: Enabling context-aware data analytics in smart environments: an open source reference implementation. Sensors 21(21) (2021). https://doi.org/10.3390/s21217095. https://www.mdpi.com/1424-8220/21/21/7095
Nie, L., Wang, X., Zhao, Q., Shang, Z., Feng, L., Li, G.: Digital twin for transportation big data: a reinforcement learning-based network traffic prediction approach. IEEE Trans. Intell. Transp. Syst., 1–11 (2023). https://doi.org/10.1109/TITS.2022.3232518
Paiva, S., Ahad, M.A., Tripathi, G., Feroz, N., Casalino, G.: Enabling technologies for urban smart mobility: recent trends, opportunities and challenges. Sensors 21(6), 2143 (2021)
Privat, G.: Guidelines for modelling with NGSI-LD (ETSI white paper) (2021)
Ramstedt, L., Krasemann, J.T., Davidsson, P.: Movement of people and goods. In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity, pp. 651–665. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-540-93813-2_24
Tirachini, A., Hensher, D.A., Rose, J.M.: Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transp. Res. Part A Policy Pract. 53, 36–52 (2013)
Tsai, T.H.: Self-evolutionary sibling models to forecast railway arrivals using reservation data. Eng. Appl. Artif. Intell. 96, 103960 (2020)
Wang, P., Chen, X., Chen, J., Hua, M., Pu, Z.: A two-stage method for bus passenger load prediction using automatic passenger counting data. IET Intel. Transp. Syst. 15(2), 248–260 (2021)
Wang, W., et al.: Introduction to digital twin technologies in transportation infrastructure management (TIM). In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity. Understanding Complex Systems, pp. 1–25. Springer, Heidelberg (2024). https://doi.org/10.1007/978-981-99-5804-7_1
Yu, B., Lam, W.H., Tam, M.L.: Bus arrival time prediction at bus stop with multiple routes. Transp. Res. Part C Emerg. Technol. 19(6), 1157–1170 (2011)
Zear, A., Singh, P.K., Singh, Y.: Intelligent transport system: a progressive review (2016)
Zhang, J., et al.: A real-time passenger flow estimation and prediction method for urban bus transit systems. IEEE Trans. Intell. Transp. Syst. 18(11), 3168–3178 (2017)
Acknowledgments
This work has been partially supported by the Spoke 9 “Digital Society & Smart Cities” of ICSC - Centro Nazionale di Ricerca in High Performance-Computing, Big Data and Quantum Computing, funded by the European Union - NextGenerationEU (PNRR-HPC, CUP: E63C22000980007).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
De Benedictis, A., Rocco di Torrepadula, F., Somma, A. (2024). A Digital Twin Architecture for Intelligent Public Transportation Systems: A FIWARE-Based Solution. In: Lotfian, M., Starace, L.L.L. (eds) Web and Wireless Geographical Information Systems. W2GIS 2024. Lecture Notes in Computer Science, vol 14673. Springer, Cham. https://doi.org/10.1007/978-3-031-60796-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-60796-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60795-0
Online ISBN: 978-3-031-60796-7
eBook Packages: Computer ScienceComputer Science (R0)