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Front. Eng    2024, Vol. 11 Issue (1) : 79-91    https://doi.org/10.1007/s42524-023-0291-z
Urban rail transit disruption management: Research progress and future directions
Lebing WANG1, Jian Gang JIN1(), Lijun SUN2, Der-Horng LEE3()
1. School of Naval Architecture, Ocean & Civil Engineering, and State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. Department of Civil Engineering, McGill University, Montreal, Quebec H3A 0C3, Canada
3. ZJU-UIUC Institute, Zhejiang University, Haining 314400, China
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Abstract

Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism; ii) substantial negative effects, notably the delay of numerous passengers; iii) an escalating frequency, attributable to the gradual aging of facilities; and iv) severe penalties, including substantial fines for abnormal operation. This article systematically reviews URT disruption management literature from the past decade, categorizing it into pre-disruption and post-disruption measures. The pre-disruption research focuses on reducing the effects of disruptions through network analysis, passenger behavior analysis, resource allocation for protection and backup, and enhancing system resilience. Conversely, post-disruption research concentrates on restoring normal operations through train rescheduling and bus bridging services. The review reveals that while post-disruption strategies are thoroughly explored, pre-disruption research is predominantly analytical, with a scarcity of practical pre-emptive solutions. Moreover, future research should focus more on increasing the interchangeability of transport modes, reinforcing redundancy relationships between URT lines, and innovating post-disruption strategies.
Keywords urban rail transit      disruption management      resilient network      train rescheduling      bus bridging services     
Corresponding Author(s): Jian Gang JIN,Der-Horng LEE   
Just Accepted Date: 17 January 2024   Online First Date: 26 February 2024    Issue Date: 13 March 2024
 Cite this article:   
Lebing WANG,Jian Gang JIN,Lijun SUN, et al. Urban rail transit disruption management: Research progress and future directions[J]. Front. Eng, 2024, 11(1): 79-91.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-023-0291-z
https://academic.hep.com.cn/fem/EN/Y2024/V11/I1/79
Fig.1  Structure of review.
Fig.2  The effect of pre- and post-disruption measures.
LiteratureStrategySpecial considerationObjectiveAlgorithm
Cadarso et al. (2013)ST, ET, CA, TO, CCPassenger behaviorOC, CA, PD, ADCPLEX
Cadarso and Marín (2014)RT, ET, CA, RSPassenger path choices and mode shiftsTD, PI, ADCPLEX
Cadarso et al. (2015)RT, CA, ET, CCLarge-scale disruptionsAD, OQCPLEX
Xu et al. (2016)RT, TOIncidents on dual-track linesTDEfficient train rescheduling strategy (ETRS); COIN-CBC
Gao et al. (2017)RTMinor faultsADSimulation system; GUROBI
Wang et al. (2018)RT, RSDynamic passengersADINP methods; CPLEX
Huang et al. (2020)TO, STDynamic passengersPI, ADTwo-stage approach; CPLEX
Besinovic et al. (2019)RT, ST, RR, CAControlling passenger flowsPD, PI, AD, TDGUROBI; Sequential quadratic programming (SQP) algorithm
Wang et al. (2021b)RT, CA, ST, RSComplete blockageAD, CATwo-stage approach; CPLEX
Chen et al. (2022)SPDisturbances; Dynamic passengersAD, OQMPC; GUROBI
Jin et al. (2022)RTDisturbances; Peak power reductionADMPC; CPLEX
Tab.1  Summary of relevant studies on train rescheduling for URT systems
LiteratureStrategySpecial considerationObjectiveAlgorithm
Louwerse and Huisman (2014)RT, RO, CA, STMajor disruptionsTD, CACPLEX
Kroon et al. (2015)RSDynamic passengerOCIterative heuristic approach
Binder et al. (2017)RT, RO, RR, ET, CA, SPMajor disruptionsOC, AD, PICPLEX; ε constraints
Veelenturf et al. (2017)SP, RSIncomplete blockage; Passengers’ free choiceOC, PD, ADIterative heuristic approach
van der Hurk et al. (2018)RSMajor disruptions; Uncertain duration; Passengers’ route advicePIIterative algorithm
Zhu and Goverde (2019)RT, RO, CA, SP, ST, RSAll phases of a disruptionPDGUROBI
Zhu and Goverde (2020)RT, RO, CA, SP, ST, RSStation capacitiesPD, PIAFaO algorithm
Zhu and Goverde (2021)RT, STMultiple disruptions occur simultaneouslyCA, ADRolling horizon solution method
Zhan et al. (2021)RT, CA, PRComplete blockageOC, PDTwo-layer decomposition; ADMM algorithm
Tab.2  Summary of relevant studies on train rescheduling for railway systems
Fig.3  Bus bridging routes.
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