基于动态客流的城市轨道交通关键站点识别

Quantitative evaluation of station importance in subway networks can help optimize the urban rail transit networks and enhance the management capability for emergencies. Several existing studies have analyzed the important stations based on the rail structure or the static distribution of passenger flows. However, the spatiotemporal characteristics of residents in daily travel also play a crucial role when evaluating the station's importance. Therefore, this paper proposes a novel evaluation method named topology-flow centrality for identifying the important stations by combining the topology of the subway networks and dynamic passenger flows. To begin with, the topology of a rail transit network is abstracted as a node load network, and the load of nodes is used to describe the time-varying characteristics of passenger flows. Then, according to cascading failure, this paper compares the topology-flow centrality and other centrality criteria on the impact of the average network efficiency and the lost passenger flow. Experimental results demonstrate that the proposed criterion can effectively identify the important stations in subway networks. Moreover, the importance of stations displays dynamism with the evolution of passenger flows, especially when the passenger flows fluctuate sharply.