Urban rail transit disruption management: Research progress and future directions

Lebing WANG , Jian Gang JIN , Lijun SUN , Der-Horng LEE

Front. Eng ›› 2024, Vol. 11 ›› Issue (1) : 79 -91.

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Front. Eng ›› 2024, Vol. 11 ›› Issue (1) : 79 -91. DOI: 10.1007/s42524-023-0291-z
Traffic Engineering Systems Management
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Urban rail transit disruption management: Research progress and future directions

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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.

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Keywords

urban rail transit / disruption management / resilient network / train rescheduling / bus bridging services

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Lebing WANG, Jian Gang JIN, Lijun SUN, Der-Horng LEE. Urban rail transit disruption management: Research progress and future directions. Front. Eng, 2024, 11(1): 79-91 DOI:10.1007/s42524-023-0291-z

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References

[1]

An, S Cui, N Li, X Ouyang, Y (2013). Location planning for transit-based evacuation under the risk of service disruptions. Transportation Research Part B: Methodological, 54: 1–16

[2]

BesinovicNWangYZhuSQuagliettaETangTGoverdeR M P (2019). Integrated train and passenger disruption management for urban railway lines. In: IEEE Intelligent Transportation Systems Conference (ITSC). Auckland: IEEE, 3182–3187

[3]

Binder, S Maknoon, Y Bierlaire, M (2017). The multi-objective railway timetable rescheduling problem. Transportation Research Part C: Emerging Technologies, 78: 78–94

[4]

Cacchiani, V Huisman, D Kidd, M Kroon, L Toth, P Veelenturf, L Wagenaar, J (2014). An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B: Methodological, 63: 15–37

[5]

Cadarso, L Codina, E Escudero, L F Marín, A (2017). Rapid transit network design: Considering recovery robustness and risk aversion measures. Transportation Research Procedia, 22: 255–264

[6]

Cadarso, L Escudero, L F Marín, A (2018). On strategic multistage operational two-stage stochastic 0–1 optimization for the Rapid Transit Network Design problem. European Journal of Operational Research, 271( 2): 577–593

[7]

Cadarso, L Marín, Á (2014). Recovery of disruptions in rapid transit networks with origin–destination demand. Procedia: Social and Behavioral Sciences, 111: 528–537

[8]

Cadarso, L Marín, Á Maróti, G (2013). Recovery of disruptions in rapid transit networks. Transportation Research Part E: Logistics and Transportation Review, 53: 15–33

[9]

Cadarso, L Maróti, G Marín, Á (2015). Smooth and controlled recovery planning of disruptions in rapid transit networks. IEEE Transactions on Intelligent Transportation Systems, 16( 4): 2192–2202

[10]

Canca, D De-Los-Santos, A Laporte, G Mesa, J A (2019). Integrated railway rapid transit network design and line planning problem with maximum profit. Transportation Research Part E: Logistics and Transportation Review, 127: 1–30

[11]

ChenJJiangCLiuXDuBPengQYinYLiB (2023). Resilience enhancement of an urban rail transit network by setting turn-back tracks: A scenario model approach. Transportation Research Record: Journal of the Transportation Research Board, in press, doi:10.1177/03611981231164066

[12]

Chen, Y An, K (2021). Integrated optimization of bus bridging routes and timetables for rail disruptions. European Journal of Operational Research, 295( 2): 484–498

[13]

Chen, Z Li, S D’Ariano, A Yang, L (2022). Real-time optimization for train regulation and stop-skipping adjustment strategy of urban rail transit lines. Omega, 110: 102631

[14]

Currie, G Muir, C (2017). Understanding passenger perceptions and behaviors during unplanned rail disruptions. Transportation Research Procedia, 25: 4392–4402

[15]

De-Los-Santos, A Laporte, G Mesa, J A Perea, F (2012). Evaluating passenger robustness in a rail transit network. Transportation Research Part C: Emerging Technologies, 20( 1): 34–46

[16]

Dou, X Wang, H Meng, Q (2019). Parallel shuttle bus service design for planned mass rapid transit shutdown: The Singapore experience. Transportation Research Part C: Emerging Technologies, 108: 340–356

[17]

Eltved, M Breyer, N Ingvardson, J B Nielsen, O A (2021). Impacts of long-term service disruptions on passenger travel behaviour: A smart card analysis from the Greater Copenhagen area. Transportation Research Part C: Emerging Technologies, 131: 103198

[18]

Gao, Y Kroon, L Schmidt, M Yang, L (2016). Rescheduling a metro line in an over-crowded situation after disruptions. Transportation Research Part B: Methodological, 93: 425–449

[19]

Gao, Y Yang, L Gao, Z (2017). Real-time automatic rescheduling strategy for an urban rail line by integrating the information of fault handling. Transportation Research Part C: Emerging Technologies, 81: 246–267

[20]

Ghaemi, N Cats, O Goverde, R M P (2017). Railway disruption management challenges and possible solution directions. Public Transport, 9( 1–2): 343–364

[21]

Gu, W Yu, J Ji, Y Zheng, Y Zhang, H M (2018). Plan-based flexible bus bridging operation strategy. Transportation Research Part C: Emerging Technologies, 91: 209–229

[22]

Guo, X Sun, H J Wu, J J Jin, J G Zhou, J Gao, Z Y (2017). Multiperiod-based timetable optimization for metro transit networks. Transportation Research Part B: Methodological, 96: 46–67

[23]

Hong, W T Clifton, G Nelson, J D (2022). Rail transport system vulnerability analysis and policy implementation: Past progress and future directions. Transport Policy, 128: 299–308

[24]

Hua, W Ong, G P (2017). Network survivability and recoverability in urban rail transit systems under disruption. IET Intelligent Transport Systems, 11( 10): 641–648

[25]

Huang, Y Mannino, C Yang, L Tang, T (2020). Coupling time-indexed and big-M formulations for real-time train scheduling during metro service disruptions. Transportation Research Part B: Methodological, 133: 38–61

[26]

Itani, A Srikukenthiran, S Shalaby, A (2020). Capacity-constrained bus bridging optimization framework. Transportation Research Record: Journal of the Transportation Research Board, 2674( 5): 600–612

[27]

Jin, B Feng, X Wang, Q Sun, P (2022). Real-time train regulation method for metro lines with substation peak power reduction. Computers & Industrial Engineering, 168: 108113

[28]

Jin, J G Lu, L Sun, L Yin, J (2015). Optimal allocation of protective resources in urban rail transit networks against intentional attacks. Transportation Research Part E: Logistics and Transportation Review, 84: 73–87

[29]

Jin, J G Tang, L C Sun, L Lee, D H (2014). Enhancing metro network resilience via localized integration with bus services. Transportation Research Part E: Logistics and Transportation Review, 63: 17–30

[30]

Jin, J G Teo, K M Odoni, A R (2016). Optimizing bus bridging services in response to disruptions of urban transit rail networks. Transportation Science, 50( 3): 790–804

[31]

Jing, W Xu, X Pu, Y (2020). Route redundancy-based approach to identify the critical stations in metro networks: A mean-excess probability measure. Reliability Engineering & System Safety, 204: 107204

[32]

Kopsidas, A Kepaptsoglou, K (2022). Identification of critical stations in a metro system: A substitute complex network analysis. Physica A, 596: 127123

[33]

Krishnakumari, P Cats, O van Lint, H (2020). Estimation of metro network passenger delay from individual trajectories. Transportation Research Part C: Emerging Technologies, 117: 102704

[34]

Kroon, L Maróti, G Nielsen, L (2015). Rescheduling of railway rolling stock with dynamic passenger flows. Transportation Science, 49( 2): 165–184

[35]

Li, B Yao, E Yamamoto, T Huan, N Liu, S (2020a). Passenger travel behavior analysis under unplanned metro service disruption: Using stated preference data in Guangzhou, China. Journal of Transportation Engineering, Part A: Systems, 146( 2): 04019069

[36]

Li, B Yao, E Yamamoto, T Tang, Y Liu, S (2020b). Exploring behavioral heterogeneities of metro passenger’s travel plan choice under unplanned service disruption with uncertainty. Transportation Research Part A: Policy and Practice, 141: 294–306

[37]

Li, Z Yin, J Chai, S Tang, T Yang, L (2023). Optimization of system resilience in urban rail systems: Train rescheduling considering congestions of stations. Computers & Industrial Engineering, 185: 109657

[38]

Liang, J Wu, J Qu, Y Yin, H Qu, X Gao, Z (2019). Robust bus bridging service design under rail transit system disruptions. Transportation Research Part E: Logistics and Transportation Review, 132: 97–116

[39]

Liu, K Zhu, J Wang, M (2021a). An event-based probabilistic model of disruption risk to urban metro networks. Transportation Research Part A: Policy and Practice, 147: 93–105

[40]

Liu, T Ma, Z Koutsopoulos, H N (2021b). Unplanned disruption analysis in urban railway systems using smart card data. Urban Rail Transit, 7( 3): 177–190

[41]

Louwerse, I Huisman, D (2014). Adjusting a railway timetable in case of partial or complete blockades. European Journal of Operational Research, 235( 3): 583–593

[42]

Luo, C Xu, L (2021). Railway disruption management: Designing bus bridging services under uncertainty. Computers & Operations Research, 131: 105284

[43]

Moccia, L Giallombardo, G Laporte, G (2017). Models for technology choice in a transit corridor with elastic demand. Transportation Research Part B: Methodological, 104: 733–756

[44]

Monsuur, F Enoch, M Quddus, M Meek, S (2021). Modelling the impact of rail delays on passenger satisfaction. Transportation Research Part A: Policy and Practice, 152: 19–35

[45]

Niu, H Zhou, X (2013). Optimizing urban rail timetable under time-dependent demand and oversaturated conditions. Transportation Research Part C: Emerging Technologies, 36: 212–230

[46]

PenderB MCurrieGDelboscA RShiwakotiN (2012). Planning for the unplanned: An international review of current approaches to service disruption management of railways. In: Australasian Transport Research Forum. Perth, 1–17

[47]

Pender, B Currie, G Delbosc, A Shiwakoti, N (2014). Improving bus bridging responses via satellite bus reserve locations. Journal of Transport Geography, 34: 202–210

[48]

Pender, B Currie, G Shiwakoti, N Delbosc, A (2015). Economic viability of bus bridging reserves for fast response to unplanned passenger rail disruption. Transportation Research Record: Journal of the Transportation Research Board, 2537( 1): 13–22

[49]

Pnevmatikou, A M Karlaftis, M G Kepaptsoglou, K (2015). Metro service disruptions: How do people choose to travel?. Transportation, 42( 6): 933–949

[50]

Sun, H Wu, J Wu, L Yan, X Gao, Z (2016). Estimating the influence of common disruptions on urban rail transit networks. Transportation Research Part A: Policy and Practice, 94: 62–75

[51]

Tan, Z Xu, M Meng, Q Li, Z C (2020). Evacuating metro passengers via the urban bus system under uncertain disruption recovery time and heterogeneous risk-taking behaviour. Transportation Research Part C: Emerging Technologies, 119: 102761

[52]

Tang, J Xu, L Luo, C Ng, T S A (2021). Multi-disruption resilience assessment of rail transit systems with optimized commuter flows. Reliability Engineering & System Safety, 214: 107715

[53]

Tessitore, M L Samà, M D’Ariano, A Hélouet, L Pacciarelli, D (2022). A simulation-optimization framework for traffic disturbance recovery in metro systems. Transportation Research Part C: Emerging Technologies, 136: 103525

[54]

van, der Hurk E Koutsopoulos, H N Wilson, N Kroon, L G Maróti, G (2016). Shuttle planning for link closures in urban public transport networks. Transportation Science, 50( 3): 947–965

[55]

van, der Hurk E Kroon, L Maróti, G (2018). Passenger advice and rolling stock rescheduling under uncertainty for disruption management. Transportation Science, 52( 6): 1391–1411

[56]

Veelenturf, L P Kroon, L G Maróti, G (2017). Passenger oriented railway disruption management by adapting timetables and rolling stock schedules. Transportation Research Part C: Emerging Technologies, 80: 133–147

[57]

Wang, J Yuan, Z Cao, Z Lu, Z (2021a). Optimal bus bridging schedule with transfer passenger demand during disruptions of urban rail transit. Journal of Transportation Engineering, Part A: Systems, 147( 10): 04021071

[58]

Wang, J Yuan, Z Yin, Y (2019). Optimization of bus bridging service under unexpected metro disruptions with dynamic passenger flows. Journal of Advanced Transportation, 6965728

[59]

Wang, L Jin, J G Sibul, G Wei, Y (2023a). Designing metro network expansion: Deterministic and robust optimization models. Networks and Spatial Economics, 23( 1): 317–347

[60]

Wang, X Jin, J G Sun, L (2022). Real-time dispatching of operating buses during unplanned disruptions to urban rail transit system. Transportation Research Part C: Emerging Technologies, 139: 103696

[61]

Wang, Y D’Ariano, A Yin, J Meng, L Tang, T Ning, B (2018). Passenger demand oriented train scheduling and rolling stock circulation planning for an urban rail transit line. Transportation Research Part B: Methodological, 118: 193–227

[62]

Wang, Y Guo, J Currie, G Ceder, A Dong, W Pender, B (2014). Bus bridging disruption in rail services with frustrated and impatient passengers. IEEE Transactions on Intelligent Transportation Systems, 15( 5): 2014–2023

[63]

Wang, Y Yan, X Zhou, Y Zhang, W (2016). A feeder-bus dispatch planning model for emergency evacuation in urban rail transit corridors. PLoS One, 11( 9): e0161644

[64]

Wang, Y Zhao, K D’Ariano, A Niu, R Li, S Luan, X (2021b). Real-time integrated train rescheduling and rolling stock circulation planning for a metro line under disruptions. Transportation Research Part B: Methodological, 152: 87–117

[65]

Wang, Y Zhou, Y Yang, H Yan, X (2023b). Integrated optimization of bus bridging service design and passenger assignment in response to urban rail transit disruptions. Transportation Research Part C: Emerging Technologies, 150: 104098

[66]

Xu, L Ng, T S Costa, A (2021a). Optimizing disruption tolerance for rail transit networks under uncertainty. Transportation Science, 55( 5): 1206–1225

[67]

Xu, X Chen, A Jansuwan, S Yang, C Ryu, S (2018). Transportation network redundancy: Complementary measures and computational methods. Transportation Research Part B: Methodological, 114: 68–85

[68]

Xu, X Chen, A Xu, G Yang, C Lam, W H K (2021b). Enhancing network resilience by adding redundancy to road networks. Transportation Research Part E: Logistics and Transportation Review, 154: 102448

[69]

Xu, X Li, K Yang, L (2016). Rescheduling subway trains by a discrete event model considering service balance performance. Applied Mathematical Modelling, 40( 2): 1446–1466

[70]

Xu, Z Chopra, S S (2022). Network-based assessment of metro infrastructure with a spatial–temporal resilience cycle framework. Reliability Engineering & System Safety, 223: 108434

[71]

Yang, J Jin, J G Wu, J Jiang, X (2017). Optimizing passenger flow control and bus-bridging service for commuting metro lines. Computer-Aided Civil and Infrastructure Engineering, 32( 6): 458–473

[72]

Yang, Z Chen, X (2019). Compensation decisions on disruption recovery service in urban rail transit. Promet, 31( 4): 367–375

[73]

Yin, H Wu, J Sun, H Qu, Y Yang, X Wang, B (2018). Optimal bus-bridging service under a metro station disruption. Journal of Advanced Transportation, 2758652

[74]

Yuan, J Gao, Y Li, S Liu, P Yang, L (2022a). Integrated optimization of train timetable, rolling stock assignment and short-turning strategy for a metro line. European Journal of Operational Research, 301( 3): 855–874

[75]

Yuan, J Jones, D Nicholson, G (2022b). Flexible real-time railway crew rescheduling using Depth-first search. Journal of Rail Transport Planning & Management, 24: 100353

[76]

Zhan, S Wong, S C Shang, P Peng, Q Xie, J Lo, S M (2021). Integrated railway timetable rescheduling and dynamic passenger routing during a complete blockage. Transportation Research Part B: Methodological, 143: 86–123

[77]

Zhang, N Graham, D J Hörcher, D Bansal, P (2021a). A causal inference approach to measure the vulnerability of urban metro systems. Transportation, 48( 6): 3269–3300

[78]

Zhang, S Lo, H K (2018). Metro disruption management: Optimal initiation time of substitute bus services under uncertain system recovery time. Transportation Research Part C: Emerging Technologies, 97: 409–427

[79]

Zhang, S Lo, H K (2020). Metro disruption management: Contracting substitute bus service under uncertain system recovery time. Transportation Research Part C: Emerging Technologies, 110: 98–122

[80]

Zhang, S Lo, H K Ng, K F Chen, G (2021b). Metro system disruption management and substitute bus service: A systematic review and future directions. Transport Reviews, 41( 2): 230–251

[81]

Zheng, S Liu, Y Lin, Y Wang, Q Yang, H Chen, B (2022). Bridging strategy for the disruption of metro considering the reliability of transportation system: Metro and conventional bus network. Reliability Engineering & System Safety, 225: 108585

[82]

Zhou, H Qi, J Yang, L Shi, J Mo, P (2022). Joint optimization of train scheduling and rolling stock circulation planning with passenger flow control on tidal overcrowded metro lines. Transportation Research Part C: Emerging Technologies, 140: 103708

[83]

Zhu, J Xu, X Wang, Z (2023). Economic evaluation of redundancy design for transportation networks under disruptions: Framework and case study. Transport Policy, 142: 70–83

[84]

Zhu, Y Goverde, R M P (2019). Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions. Transportation Research Part B: Methodological, 123: 149–181

[85]

Zhu, Y Goverde, R M P (2020). Integrated timetable rescheduling and passenger reassignment during railway disruptions. Transportation Research Part B: Methodological, 140: 282–314

[86]

Zhu, Y Goverde, R M P (2021). Dynamic railway timetable rescheduling for multiple connected disruptions. Transportation Research Part C: Emerging Technologies, 125: 103080

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