Abstract
A metro disruption is a situation where metro service is suspended for some time due to unexpected events such as equipment failure and extreme weather. Metro disruptions reduce the level of service of metro systems and leave numerous passengers stranded at disrupted stations. As a means of disruption management, bus bridging has been widely used to evacuate stranded passengers. This paper focuses on the bus bridging problem under operational disruptions on a single metro line. Unlike previous studies, we consider dynamic passenger flows during the disruption. A multi-objective optimization model is established with objectives to minimize total waiting time, the number of stranded passengers and dispatched vehicles with constraints such as fleet size and vehicle capacity. The NSGA-II algorithm is used for the solution. Finally, we apply the proposed model to Shanghai Metro to access the effectiveness of our approaches in comparison with the current bridging strategy. Sensitivity analysis of the bus fleet size involved in the bus bridging problem was conducted.
Highlights
Metro disruptions, usually caused by emergency events such as infrastructure blockages, accidents and extreme weather, have frequently occurred in lots of cities [1]
When the disruption lasts for a long time, for the purpose of reducing the impacts of disruptions, the affected metro line tends to operate in a short turning mode, on residual railway sections beyond the track crossovers
We further investigate the impact of recovery time and fleet size on results
Summary
Usually caused by emergency events such as infrastructure blockages, accidents and extreme weather, have frequently occurred in lots of cities [1]. Bus bridging operation strategies proposed to deal with unexpected disruptions did not require buses to operate based on fixed frequencies [1]. Hu et al [16] proposed an efficient bus bridging strategy and constructed a multi-circle bus dispatching model minimizing the total evacuation time without taking dynamic passenger demands into account. Gu et al [1] proposed a flexible strategy allocating and scheduling buses to different bridging routes They formulated a twostep model to optimize the bus bridging schedule minimizing total evacuation time and passenger delay, respectively. Contributions of our research are summarized as follows: (i) an optimization-based model was proposed considering dynamic passenger flow demands during the disruption.
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