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2021 | OriginalPaper | Buchkapitel

Role of Artificial Intelligence in Railways: An Overview

verfasst von : Neeraj Kumar, Abhishek Mishra

Erschienen in: Advances in Industrial and Production Engineering

Verlag: Springer Singapore

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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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Literatur
1.
Zurück zum Zitat 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) 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)
2.
Zurück zum Zitat Dougherty, M.: A review of neural networks applied to transport. Transp. Res. Part C: Emerg. Technol. 3(4), 247–260 (1995)CrossRef Dougherty, M.: A review of neural networks applied to transport. Transp. Res. Part C: Emerg. Technol. 3(4), 247–260 (1995)CrossRef
3.
Zurück zum Zitat Furutani, R., Kudo, F. and Moriwaki, N.: Case study of energy efficiency in railway operations. Hitachi Rev. 65(6), 129 (2016) Furutani, R., Kudo, F. and Moriwaki, N.: Case study of energy efficiency in railway operations. Hitachi Rev. 65(6), 129 (2016)
4.
Zurück zum Zitat Horwitz, D., El-Sibaie, M.: Applying neural nets to railway engineering. AI Expert 10(1), 36–43 (1995) Horwitz, D., El-Sibaie, M.: Applying neural nets to railway engineering. AI Expert 10(1), 36–43 (1995)
5.
Zurück zum Zitat Szpigel, B.: Optimal train scheduling on a single line railway. 344–351 (1973) Szpigel, B.: Optimal train scheduling on a single line railway. 344–351 (1973)
6.
Zurück zum Zitat 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)CrossRef 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)CrossRef
7.
Zurück zum Zitat Narayanaswami, S., Rangaraj, N.: Scheduling and rescheduling of railway operations: a review and expository analysis. Technol. Oper. Manage. 2(2), 102–122 (2011)CrossRef Narayanaswami, S., Rangaraj, N.: Scheduling and rescheduling of railway operations: a review and expository analysis. Technol. Oper. Manage. 2(2), 102–122 (2011)CrossRef
8.
Zurück zum Zitat 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) 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)
9.
Zurück zum Zitat 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) 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)
10.
Zurück zum Zitat 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)CrossRef 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)CrossRef
11.
Zurück zum Zitat Martinelli, D.R., Teng, H.: Optimization of railway operations using neural networks. Transp. Res. Part C: Emerg. Technol. 4(1), 33–49 (1996)CrossRef Martinelli, D.R., Teng, H.: Optimization of railway operations using neural networks. Transp. Res. Part C: Emerg. Technol. 4(1), 33–49 (1996)CrossRef
12.
Zurück zum Zitat Cucala, A.P., Fernández, A., Sicre, C., Domínguez, 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)CrossRef Cucala, A.P., Fernández, A., Sicre, C., Domínguez, 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)CrossRef
13.
Zurück zum Zitat 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)CrossRef 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)CrossRef
14.
15.
Zurück zum Zitat Petersen, E.R., Taylor, A.J., Martland, C.D.: An introduction to computer-assisted train dispatch. J. Adv. Transp. 20(1), 63–72 (1986)CrossRef Petersen, E.R., Taylor, A.J., Martland, C.D.: An introduction to computer-assisted train dispatch. J. Adv. Transp. 20(1), 63–72 (1986)CrossRef
16.
Zurück zum Zitat Caimi, G.C.: Algorithmic decision suport for train scheduling in a large and highly utilised railway network. ETH Zurich (2009) Caimi, G.C.: Algorithmic decision suport for train scheduling in a large and highly utilised railway network. ETH Zurich (2009)
17.
Zurück zum Zitat 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)CrossRef 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)CrossRef
18.
Zurück zum Zitat 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)CrossRef 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)CrossRef
19.
Zurück zum Zitat Corman, F., D’Ariano, A., Hansen, I.A.: Evaluating disturbance robustness of railway schedules. J. Intell. Transp. Syst. 18(1), 106–120 (2014)CrossRef Corman, F., D’Ariano, A., Hansen, I.A.: Evaluating disturbance robustness of railway schedules. J. Intell. Transp. Syst. 18(1), 106–120 (2014)CrossRef
20.
Zurück zum Zitat Khadilkar, H.: Data-enabled stochastic modeling for evaluating schedule robustness of railway networks. Transp. Sci. 51(4), 1161–1176 (2017)CrossRef Khadilkar, H.: Data-enabled stochastic modeling for evaluating schedule robustness of railway networks. Transp. Sci. 51(4), 1161–1176 (2017)CrossRef
21.
Zurück zum Zitat 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)CrossRef 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)CrossRef
22.
Zurück zum Zitat Şahin, İ.: Railway traffic control and train scheduling based on inter-train conflict management. Transp. Res. Part B: Methodol. 33(7), 511–534 (1999)CrossRef Şahin, İ.: Railway traffic control and train scheduling based on inter-train conflict management. Transp. Res. Part B: Methodol. 33(7), 511–534 (1999)CrossRef
23.
Zurück zum Zitat 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)CrossRef 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)CrossRef
24.
Zurück zum Zitat 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)CrossRef 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)CrossRef
25.
Zurück zum Zitat Lu, S., Hillmansen, S., Ho, T.K., Roberts, C.: Single-train trajectory optimization. IEEE Trans. Intell. Transp. Syst. 14(2), 743–750 (2013)CrossRef Lu, S., Hillmansen, S., Ho, T.K., Roberts, C.: Single-train trajectory optimization. IEEE Trans. Intell. Transp. Syst. 14(2), 743–750 (2013)CrossRef
26.
Zurück zum Zitat 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)CrossRef 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)CrossRef
Metadaten
Titel
Role of Artificial Intelligence in Railways: An Overview
verfasst von
Neeraj Kumar
Abhishek Mishra
Copyright-Jahr
2021
Verlag
Springer Singapore
DOI
https://doi.org/10.1007/978-981-33-4320-7_29

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