地铁网络火灾协同救援调度模型

doi:10.3976/j.issn.1002-4026.2025004

摘要/Abstract

摘要： 针对地铁火灾场景下地铁消防车（MFEs）与列车的线网综合调度难题，鉴于地铁隧道空间局限、常规消防车难以介入，且火灾发生位置的不确定性，需要MFEs跨线调度。面向地铁消防车救援和被困列车疏散两个子问题，分别建立了混合整数规划模型。为了提高求解效率，提出了基于线路的分解算法和过滤约束算法。以苏州地铁线网为算例对模型进行验证。结果表明，所提出的算法能够在接受时间内完成求解，并且能够实现地铁消防车的跨线救援和被困列车的快速疏散。本文不仅为地铁火灾紧急情况下的调度提供了理论支持，也为实际的地铁运营管理提供了实用的决策工具，具有重要的理论意义和应用价值。

关键词: 地铁消防车, 地铁火灾救援, 列车运行调整, 混合整数规划, 越行

Abstract: The integrated dispatch of metro firefighting engines (MFEs) and trains during fire emergencies presents unique challenges due to confined tunnel spaces that restrict conventional fire truck access and the often uncertain fire location. This necessitates cross-line deployment of MFEs within the metro network. We decompose the problem into two subproblems: MFE rescue operations and evacuation of trapped trains. For each, a mixed-integer programming model is formulated. To enhance computational efficiency, we propose a line-based decomposition algorithm combined with a constraint filtering technique. The models are validated using the Suzhou metro network as a case study. Results demonstrate that the proposed approach efficiently generates solutions within practical timeframes, enabling effective cross-line MFE dispatch and rapid train evacuation. This research offers both theoretical insights and a practical decision-support tool for emergency management in metro fire scenarios, contributing significant value to metro operation safety and resilience.

Key words: metro firefighting engine, fire rescue, train operation adjustment, mean-integer programming, cross-line dispatch

中图分类号:

U291

季盛逸, 张勇. 地铁网络火灾协同救援调度模型[J]. 山东科学, 0, (): 1-.

JI Shengyi, ZHANG Yong. Collaborative rescue dispatch model for metro network fire emergencies[J]. Shandong Science, 0, (): 1-.

参考文献

[1] 韩宝明, 余怡然, 习喆, 等. 2023年世界城市轨道交通运营统计与分析综述[J]. 都市快轨交通, 2024, 37(1):1-9. DOI:10.3969/j.issn.1672-6073.2024.01.001.

[2] ZHENG K, ZHANG Y. Optimization of rapid rescue for metro fire based on the metro fire engine[C]//2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 【◆◆◆Macau◆◆◆】, China. IEEE, 2022: 1795-1802. DOI: 10.1109/ITSC55140.2022.9922007.

[3] WANG J H, YAN W Y, XU H, et al. Investigation of the probability of a safe evacuation to succeed in subway fire emergencies based on Bayesian theory[J]. KSCE Journal of Civil Engineering, 2018, 22(3): 877-886. DOI: 10.1007/s12205-018-0620-7.

[4] YU L X, LIU F, LIU Y Q, et al. Experimental study on thermal and smoke control using transverse ventilation in a sloping urban traffic link tunnel fire[J]. Tunnelling and Underground Space Technology, 2018, 71: 81-93. DOI: 10.1016/j.tust.2017.08.012.

[5] ZHAN S G, XIE J M, WONG S C, et al. Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions[J]. Transportation Research Part E: Logistics and Transportation Review, 2024, 183: 103429. DOI: 10.1016/j.tre.2024.103429.

[6] CACCHIANI V, HUISMAN D, KIDD M, et al. An overview of recovery models and algorithms for real-time railway rescheduling[J]. Transportation Research Part B: Methodological, 2014, 63: 15-37. DOI: 10.1016/j.trb.2014.01.009.

[7] ZHAN S G, KROON L G, ZHAO J, et al. A rolling horizon approach to the high speed train rescheduling problem in case of a partial segment blockage[J]. Transportation Research Part E: Logistics and Transportation Review, 2016, 95: 32-61. DOI: 10.1016/j.tre.2016.07.015.

[8] XU X, LI K, Yang L. Rescheduling subway trains by a discrete event model considering[J]. Applied Mathematical Modelling, 2015, 40(2). DOI： 10.1016/j.apm.2015.06.031

[9] LÖVÉTEI I F, LINDENMAIER L, ARADI S. Efficient real-time rail traffic optimization: Decomposition of rerouting, reordering, and rescheduling problems[J]. Journal of Rail Transport Planning & Management, 2025, 33: 100496. DOI: 10.1016/j.jrtpm.2024.100496.

[10] HUANG Y R, MANNINO C, YANG L X, et al. Coupling time-indexed and big-M formulations for real-time train scheduling during metro service disruptions[J]. Transportation Research Part B: Methodological, 2020, 133: 38-61. DOI: 10.1016/j.trb.2019.12.005.

[11] SCHETTINI T, JABALI O, MALUCELLI F. Demand-driven timetabling for a metro corridor using a short-turning acceleration strategy[J]. Transportation Science, 2022, 56(4): 919-937. DOI: 10.1287/trsc.2021.1118.

[12] GAO Y, KROON L, SCHMIDT M, et al. Rescheduling a metro line in an over-crowded situation after disruptions[J]. Transportation Research Part B: Methodological, 2016, 93: 425-449. DOI: 10.1016/j.trb.2016.08.011.

[13] ZHU Y Q, GOVERDE R M P. Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions[J]. Transportation Research Part B: Methodological, 2019, 123: 149-181. DOI: 10.1016/j.trb.2019.02.015.

[14] GHAEMI N, CATS O, GOVERDE R M P. Macroscopic multiple-station short-turning model in case of complete railway blockages[J]. Transportation Research Part C: Emerging Technologies, 2018, 89: 113-132. DOI: 10.1016/j.trc.2018.02.006.

[15] PENG S R, YANG X, DING S X, et al. A dynamic rescheduling and speed management approach for high-speed trains with uncertain time-delay[J]. Information Sciences, 2023, 632: 201-220. DOI: 10.1016/j.ins.2023.03.003.

[16] YIN J T, YANG L X, TANG T, et al. Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches[J]. Transportation Research Part B: Methodological, 2017, 97: 182-213. DOI: 10.1016/j.trb.2017.01.001.

[17] ZHAN S G, WONG S C, SHANG P, et al. Integrated railway timetable rescheduling and dynamic passenger routing during a complete blockage[J]. Transportation Research Part B: Methodological, 2021, 143: 86-123. DOI: 10.1016/j.trb.2020.11.006.

[18] ZHANG C T, GAO Y, CACCHIANI V, et al. Train rescheduling for large-scale disruptions in a large-scale railway network[J]. Transportation Research Part B: Methodological, 2023, 174: 102786. DOI: 10.1016/j.trb.2023.102786.

[19] PU F, YIN J T, WANG Y H, et al. Rolling stock allocation and timetabling for urban rail transit network with multiple depots[J]. Transportation Research Record: Journal of the Transportation Research Board, 2022, 2676(11): 422-435. DOI: 10.1177/03611981221093323.

[20] YIN J T, PU F, YANG L X, et al. Integrated optimization of rolling stock allocation and train timetables for urban rail transit networks: A benders decomposition approach[J]. Transportation Research Part B: Methodological, 2023, 176: 102815. DOI: 10.1016/j.trb.2023.102815.

[21] PAN H C, YANG L X, LIANG Z. Demand-oriented integration optimization of train timetabling and rolling stock circulation planning with flexible train compositions: A column-generation-based approach[J]. European Journal of Operational Research, 2023, 305(1): 184-206. DOI: 10.1016/j.ejor.2022.05.039.

[22] CADARSO L, MARÍN Á, MARÓTI G. Recovery of disruptions in rapid transit networks[J]. Transportation Research Part E: Logistics and Transportation Review, 2013, 53: 15-33. DOI: 10.1016/j.tre.2013.01.013.

[23] SUN L S, LU H B, XU Y, et al. Fairness-oriented train service design for urban rail transit cross-line operation[J]. Physica A: Statistical Mechanics and its Applications, 2022, 606: 128124. DOI: 10.1016/j.physa.2022.128124.

[24] CHAI S M, YIN J T, D’ARIANO A, et al. Scheduling of coupled train platoons for metro networks: A passenger demand-oriented approach[J]. Transportation Research Record: Journal of the Transportation Research Board, 2023, 2677(2): 1671-1689. DOI: 10.1177/03611981221109175.

[25] CHAI S M, YIN J T, D’ARIANO A, et al. Train schedule optimization for commuter-metro networks[J]. Transportation Research Part C: Emerging Technologies, 2023, 155: 104278. DOI: 10.1016/j.trc.2023.104278.

[26] WANG E T, YANG L X, YIN J T, et al. Passenger-oriented rolling stock scheduling in the metro system with multiple depots: Network flow based approaches[J]. Transportation Research Part B: Methodological, 2024, 180: 102885. DOI: 10.1016/j.trb.2024.102885.

[27] WANG Y H, ZHAO K Q, D’ARIANO A, et al. Real-time integrated train rescheduling and rolling stock circulation planning for a metro line under disruptions[J]. Transportation Research Part B: Methodological, 2021, 152: 87-117. DOI: 10.1016/j.trb.2021.08.003.

[28] BARRENA E, CANCA D, COELHO L C, et al. Single-line rail rapid transit timetabling under dynamic passenger demand[J]. Transportation Research Part B: Methodological, 2014, 70: 134-150. DOI: 10.1016/j.trb.2014.08.013.

[29] ZHENG Y J, ZHANG M X, ZHANG B. Biogeographic harmony search for emergency air transportation[J]. Soft Computing, 2016, 20(3): 967-977. DOI: 10.1007/s00500-014-1556-6.

[30] YUAN J W, GAO Y, LI S K, et al. Integrated optimization of train timetable, rolling stock assignment and short-turning strategy for a metro line[J]. European Journal of Operational Research, 2022, 301(3): 855-874. DOI: 10.1016/j.ejor.2021.11.019.

[31] LAMORGESE L, MANNINO C. An exact decomposition approach for the real-time train dispatching problem[J]. Operations Research, 2015, 63(1): 48-64. DOI: 10.1287/opre.2014.1327.