Urban rail transit disruption management based on passenger guidance and extended bus bridging service considering uncertain bus running time

Abstract

Disruptions occurring at an urban rail transit (URT) system can severely affect its normal operations, and an effective bus bridging service (BBS) is able to help to reduce the negative effects. Transit operators usually arrange BBS to depart from the disrupted stations to evacuate the stranded passengers. However, the overload of passengers at the disrupted stations, especially at turnover and transfer stations, may incur the secondary operation disruptions such as stampede accidents. To mitigate the negative effects of disruptions and reduce the number of passengers stranded at the disrupted stations, the strategy based on the passenger guidance and extended BBS (E-BBS) is introduced in this paper. Different from the widely applied standard BBS running within the disrupted links, E-BBS runs among the normal operating stations or between the normal operating stations and the disrupted stations. A mixed integer linear programming model is developed in this paper to guide the stranded passengers and design an optimal E-BBS solution to transport them. Considering the bi-directional running trains along the disrupted links, a dynamic decision framework is developed to manage the disruptions. Given the impacts of the high uncertainty on bus running time which could affect the performance of E-BBS, a robust model is proposed to obtain more reliable travel guidance and E-BBS schemes. Numerical experiments based on Chengdu subway system in China are conducted. The results indicate that the proposed model can obtain optimal travel guidance and E-BBS solutions in a timely manner. When the uncertainty on bus running time is ignored, the total travel cost for affected passengers is reduced 14.20 % on average with the aid of the optimal travel guidance and E-BBS solutions. Moreover, the number of passengers gathering at the disrupted stations decreases by 36.80 %. The robust model can obtain more reliable travel guidance and E-BBS schemes in consideration of uncertain bus running time. The proposed model shows great potential to effectively mitigate the negative effects of disruptions and help to enhance the capability of a URT system to respond to disruptions.