Study on the topology and dynamics of the rail transit network based on automatic fare collection data

Studying the topology of a rail transit network based on complex network theory is of great significance for identifying the weak links of the network and improving the network’s accessibility and connectivity. Transfer stations play important roles in the network. This study investigates the dynamic properties of the transfer network of the Beijing rail transit system considering the passenger flow assignment. The P-space representation and automatic fare collection data were employed to build a directed and weighted network. The improved degree centrality, closeness centrality, betweenness centrality and the PageRank index were used to distinguish the key stations and sections of the network. A community detection method, the Infomap algorithm, was applied to partition the network into some subnetworks according to the dynamics of passenger flow. The results showed that a large majority of the important stations are in large residential areas or commercial office areas, located at the intersections of the radial lines with the loop lines. According to the travel demand of passengers, the network was divided into 8 communities. The majority of the trips start and end in the same community. This study could help effectively identify the important stations and sections as well as the community structures generated by passenger mobility.