Critical stations identification in urban rail transit network considering multilayer attributes

Urban rail transit stations are crucial components of the urban rail transit network as they connect lines in the network structure and facilitate the collection and distribution of passenger flow during network operations. Effectively identifying critical stations that play a vital role in maintaining network structure stability and passenger flow transportation can help reduce operational risks and improve operational service levels. In this study, we propose a new perspective for assessing the importance of stations in the rail transit network and identifying critical stations by considering the characteristics of network structure and passenger flow. We apply the theory of multi-layer complex networks and treat the urban rail network as a multi-layer network consisting of facility layer, service layer, and passenger flow layer. By analyzing the attributes of each layer, we construct node importance indicators for each layer of the network. We integrate the facility and service layers based on their similar attributes to derive a node importance indicator for the facility-service layer, which can be used to identify critical stations in unweighted topology networks. Additionally, we fuse the attributes of the passenger flow layer using linear fusion to construct a node importance indicator for the multilayer network (NIMN). To assess the effectiveness of the node importance indicators in each layer and the NIMN indicator, we evaluate critical stations using indicators such as the largest connected percentage, network efficiency, and weighted network efficiency. Simulation results using Suzhou Rail Transit as an example demonstrate that the proposed method can effectively identify critical stations in both unweighted topology networks and multi-layer networks of urban rail transit.