Analysis of the Influence and Propagation Law of Urban Rail Transit Disruptions: A Case Study of Beijing Rail Transit

Abstract

In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers.

Keywords

• urban rail transit
• disruption
• signal interruption
• AFC data