Multi-disruption resilience assessment of rail transit systems with optimized commuter flows. (October 2021)

• Record Type: Journal Article
• Authors: Tang, Junqing; Xu, Lei; Luo, Chunling; Ng, Tsan Sheng Adam
• Is Part Of: Reliability engineering & system safety. Volume 214(2021)
• Publication Date: 2021-10
• Subjects: Resilience -- Rail transit systems -- Bus-bridging services -- Strategic recovery -- Linear programming model; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107715
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: This paper studies the resilient performance of urban rail transit systems by extending a linear programming optimization model, which can provide optimized commuter flows with contingency routing under multiple disruptions. The rail transit systems in Singapore and Chongqing (China) are selected as case studies, and the system resilience and the effectiveness of providing bus-bridging services are comparatively studied. Intuitively, failures of the interchange stations would inevitably lead to a significant loss of resilience due to deteriorated network connectivity. However, the Singapore case shows that attacking those interchange stations does not always result in an extensive resilience loss. Comparing with the Chongqing case, it is discernible that the position of interchange stations would affect the system resilience. The numerical simulations reveal that the positive effect of providing bus-bridging strategy, in terms of improving the system resilience, is heterogeneous in different systems, which varies from 14% to 30% on average. As demonstrated in the sensitivity on travel demands, this positive effect is robust, yet dynamic, indicating that there is no one-size-fits-all solution for designing transfer-based recovery strategies in disruption management of rail transit systems. The managerial implications for decision makers are also provided and discussed in terms of infrastructure planning. Highlights: A newly improved linear programming optimization model is developed. Topology-based and transfer-based recovery scenarios are simulated. Resilience of the systems is quantitatively studied at platform level. The spatial distribution pattern of interchange stations might affect system resilience. Bus-bridging services improve the system resilience by 14%–30% in the case studies.