English
Język Polski
Research on emergency bus shuttle dispatch under sudden interruption of urban rail transit
DOI:
https://doi.org/10.24425/ace.2025.155107Abstract
Rail transit systems, fundamental to urban mobility, frequently encounter disruptions necessitating prompt and effective emergency responses, particularly for connecting bus services that transport passengers to affected rail lines. This research paper explores emergency dispatch methods for abnormal connecting buses in urban rail transit, concentrating on enhancing the responsiveness and efficiency of dispatch protocols during non-standard operational scenarios. By delineating the emergency shuttle service process and identifying key factors, a shuttle bus emergency dispatch model was developed for both single-line and multi-line emergency scenarios, considering passenger travel behavior and vehicle operation modes. The decision variables included the stopping plan, dispatch quantity, and departure frequency, with the objective of minimizing total passenger travel time. Constraints related to resources, time, demand, safety, and physical limitations were incorporated. Given the integer nature of the decision variables concerning the number of vehicles dispatched and the stopping plan, a solution process was designed using a discrete particle swarm optimization (DPSO) algorithm, and the model was subsequently solved.References
[1] T. Litman, “Evaluating rail transit benefits: A comment”, Transport Policy, vol. 14, no. 1, pp. 94-97, 2007, doi: 10.1016/j.tranpol.2006.09.003.
[2] J. Zhao, J. Liu, L. Yang, B. Ai, and S. Ni, “Future 5G-oriented system for urban rail transit: Opportunities and challenges”, China Communications, vol. 18, no. 2, pp. 1-12, 2021, doi: 10.23919/JCC.2021.02.001.
[3] I. Kim, G.S. Larue, L. Ferreira, A. Rakotonirainy, and K. Shaaban, “Traffic safety at road–rail level crossings using a driving simulator and traffic simulation”, Transportation Research Record, vol. 2476, pp. 109-118, 2015, doi: 10.3141/2476-15.
[4] M. Blumenfeld, W. Wemakor, L. Azzouz, and C. Roberts, “Developing a new technical strategy for rail infrastructure in low-income countries in Sub-Saharan Africa and South Asia”, Sustainability, vol. 11, no. 16, art. no. 4319, 2019, doi: 10.3390/su11164319.
[5] H. Nakamura, “How to deal with revolutions in train control systems”, Engineering, vol. 2, no. 3, pp. 380-386, 2016, doi: 10.1016/J.ENG.2016.03.015.
[6] I. Tomar, I. Sreedevi, and N. Pandey, “PLC and SCADA based Real Time Monitoring and Train Control System for the Metro Railways Infrastructure”, Wireless Personal Communications, vol. 129, pp. 521-548, 2023, doi: 10.1007/s11277-022-10109-1.
[7] N. Davari, B. Veloso, G.D.A. Costa, P.M. Pereira, R.P. Ribeiro, and J. Gama, “A survey on data-driven predictive maintenance for the railwayindustry”, Sensors, vol. 21, no. 17, art. no. 5739, 2021, doi: 10.3390/s21175739.
[8] T. Funk, V. Hromádka, J. Korytárová, and E. Vítková, “Accident costs on the railway network in the Czech national conditions”, Archives of Civil Engineering, vol. 68, no. 1, pp. 579-593, 2022, doi: 10.24425/ace.2022.140187.
[9] H. Dong, X. Liu, M. Zhou,W. Zheng, J. Xun, S. Gao, H. Song, Y. Li, and F.Y.Wang, “Integration of train control and online rescheduling for high-speed railways in case of emergencies”, IEEE Transactions on Computational Social Systems, vol. 9, no. 5, pp. 1574-1582, 2022, doi: 10.1109/TCSS.2021.3119944.
[10] C. Fang, L. Zhu, Z.G. Liu, Y.F. Li, and Y.C. Huang, “Research on rail transit dispatcher emergency decision support based on case similarity matching”, Urban Rail Transit, vol. 8, pp. 146-156, 2022, doi: 10.1007/s40864-022-00170-1.
[11] D.A. Buck, J.E. Trainor, and B.E. Aguirre, “A critical evaluation of the incident command system and NIMS”, Journal of Homeland Security and Emergency Management, vol. 3, no. 3, 2006, doi: 10.2202/1547-7355.1252.
[12] A. Holgersson, “Review of on-scene management of mass-casualty attacks”, Journal of Human Security, vol. 12, pp. 91-111, 2016, doi: 10.12924/johs2016.12010091.
[13] S.N. Blomberg, F. Folke, A.K. Ersbøll, et al., “Machine learning as a supportive tool to recognize cardiac arrest in emergency calls”, Resuscitation, vol. 138, pp. 322-329, 2019, doi: 10.1016/j.resuscitation.2019.01.015.
[14] N. Raaber, I. Duvald, I. Riddervold, E.F. Christensen, and H. Kirkegaard, “Geographic information system data from ambulances applied in the emergency department: effects on patient reception”, Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, vol. 24, pp. 1-9, 2016, doi: 10.1186/s13049-016-0232-5.
[15] J. Friesen, R. Kharel, and P.G. Delaney, “Emergency medical dispatch technologies: Addressing communication challenges and coordinating emergency response in low and middle-income countries”, Surgery, vol. 176, no. 1, pp. 223-225, 2024, doi: 10.1016/j.surg.2024.02.031.
[16] D. Peng, C. Ye, and M. Wan, “A multi-objective improved novel discrete particle swarm optimization for emergency resource center location problem”, Engineering Applications of Artificial Intelligence, vol. 111, art. no. 104725, 2022, doi: 10.1016/j.engappai.2022.104725.
Downloads
Published
How to Cite
Issue
Section
