LAUSR

In the bus network, key bus station failure can interrupt transfer lines, which leads to the low effectiveness of the whole network, especially during peak hours. Thus, identifying key stations in the bus network before the emergency occurs has a great significance to improve the response speed. In this paper, we proposed a new method considering station hybrid influence and passenger flow to identify key stations in the whole bus network. This method aims to measure the influence of bus stations while combining the topological structure of the bus network and dynamic bus stations passenger flow. The influence of bus stations was calculated based on the local structure of the network, which refines from finding the shortest paths with high computational complexity. To evaluate the performance of the method, we used the efficiency of the network and vehicle average speed at the station to examine the accuracy. The results show that the new method can rank the influence of bus stations more accurately and more efficiently than other complex network methods such as degree, H-index, and betweenness. On this basis, the key stations of the bus network of Beijing in China are identified out and the distribution characteristics of the key bus stations are analyzed.