Abstract
The worldwide increase in population joined with urbanization and a more appeal for versatility has pressurized the railroad systems of the world. The solution to this problem is to develop the infrastructure or enhancing the software with the integration of the internet for providing better services to the passengers. The combination of these three aspects of a railway system formed the Artificial Intelligence (AI). The objective of this work is to explore the role of AI in railway Transportation. The overview concludes by addressing the challenges and limitations of AI applications in railway transportation.
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References
Affrin, K., Reshma, P., Kumar, G.N.: Intelligent rescheduling trains for air pollution management. In: ICREM 2017: 19th International Conference on Railway Engineering and Management, p. 201 (2017)
Dougherty, M.: A review of neural networks applied to transport. Transp. Res. Part C: Emerg. Technol. 3(4), 247–260 (1995)
Furutani, R., Kudo, F. and Moriwaki, N.: Case study of energy efficiency in railway operations. Hitachi Rev. 65(6), 129 (2016)
Horwitz, D., El-Sibaie, M.: Applying neural nets to railway engineering. AI Expert 10(1), 36–43 (1995)
Szpigel, B.: Optimal train scheduling on a single line railway. 344–351 (1973)
Higgins, A., Kozan, E., Ferreira, L.: Optimal scheduling of trains on a single line track. Transp. Res. Part B: Methodol. 30(2), 147–161 (1996)
Narayanaswami, S., Rangaraj, N.: Scheduling and rescheduling of railway operations: a review and expository analysis. Technol. Oper. Manage. 2(2), 102–122 (2011)
Vilela, P., Cachoni, M., Vieira, A., Christofoletti, L.: Train circulation planning: quantitative approaches. In: Joint Rail Conference American Society of Mechanical Engineers Digital Collection (2017)
Nakhaee, M.C., Hiemstra, D., Stoelinga, M., van Noort, M.: The recent applications of machine learning in rail track maintenance: a survey. In: International Conference on Reliability, Safety, and Security of Railway Systems. Springer, Cham, pp. 91–105 (2019)
Wen, C., Huang, P., Li, Z., Lessan, J., Fu, L., Jiang, C., Xu, X.: Train dispatching management with data-driven approaches: a comprehensive review and appraisal. IEEE Access 7, 114547–114571 (2019)
Martinelli, D.R., Teng, H.: Optimization of railway operations using neural networks. Transp. Res. Part C: Emerg. Technol. 4(1), 33–49 (1996)
Cucala, A.P., Fernández, A., Sicre, C., DomÃnguez, M.: Fuzzy optimal schedule of high speed train operation to minimize energy consumption with uncertain delays and driver’s behavioral response. Eng. Appl. Artif. Intell. 25(8), 1548–1557 (2012)
Sinha, S.K., Salsingikar, S., SenGupta, S.: An iterative bi-level hierarchical approach for train scheduling. J. Rail Transp. Plann. Manage. 6(3), 183–199 (2016)
Mees, A.I.: Railway scheduling by network optimization. Math. Comput. Model. 15(1), 33–42 (1991)
Petersen, E.R., Taylor, A.J., Martland, C.D.: An introduction to computer-assisted train dispatch. J. Adv. Transp. 20(1), 63–72 (1986)
Caimi, G.C.: Algorithmic decision suport for train scheduling in a large and highly utilised railway network. ETH Zurich (2009)
Dollevoet, T., Corman, F., D’Ariano, A., Huisman, D.: An iterative optimization framework for delay management and train scheduling. Flex. Serv. Manuf. J. 26(4), 490–515 (2014)
Corman, F., D’Ariano, A., Marra, A.D., Pacciarelli, D., Samà , M.: Integrating train scheduling and delay management in real-time railway traffic control. Transp. Res. Part E: Logist. Transp. Rev. 105, 213–239 (2017)
Corman, F., D’Ariano, A., Hansen, I.A.: Evaluating disturbance robustness of railway schedules. J. Intell. Transp. Syst. 18(1), 106–120 (2014)
Khadilkar, H.: Data-enabled stochastic modeling for evaluating schedule robustness of railway networks. Transp. Sci. 51(4), 1161–1176 (2017)
Corman, F., D’Ariano, A., Pacciarelli, D., Pranzo, M.: Bi-objective conflict detection and resolution in railway traffic management. Transp. Res. Part C: Emerg. Technol. 20(1), 79–94 (2012)
Şahin, İ.: Railway traffic control and train scheduling based on inter-train conflict management. Transp. Res. Part B: Methodol. 33(7), 511–534 (1999)
Dündar, S., Şahin, İ.: Train re-scheduling with genetic algorithms and artificial neural networks for single-track railways. Transp. Res. Part C: Emerg. Technol. 27, 1–15 (2013)
D’Ariano, A., Pranzo, M., Hansen, I.A.: Conflict resolution and train speed coordination for solving real-time timetable perturbations. IEEE Trans. Intell. Transp. Syst. 8(2), 08–222 (2007)
Lu, S., Hillmansen, S., Ho, T.K., Roberts, C.: Single-train trajectory optimization. IEEE Trans. Intell. Transp. Syst. 14(2), 743–750 (2013)
Yin, J., Chen, D., Li, L.: Intelligent train operation algorithms for subway by expert system and reinforcement learning. IEEE Trans. Intell. Transp. Syst. 15(6), 2561–2571 (2014)
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Kumar, N., Mishra, A. (2021). Role of Artificial Intelligence in Railways: An Overview. In: Phanden, R.K., Mathiyazhagan, K., Kumar, R., Paulo Davim, J. (eds) Advances in Industrial and Production Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-33-4320-7_29
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