期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 214, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2021.107715
关键词
Resilience; Rail transit systems; Bus-bridging services; Strategic recovery; Linear programming model
资金
- National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme
- Energy and Environmental Sustainability for Megacities (E2S2) program of the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme
- Ove Arup Foundation, UK [RG89525]
This study examines the resilient performance of urban rail transit systems in Singapore and Chongqing, China by comparing the effectiveness of providing bus-bridging services and identifying the impact of interchange station locations on system resilience. The research indicates that attacking interchange stations may not always result in extensive resilience loss, and numerical simulations show that the positive effect of bus-bridging strategy varies between systems.
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.
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