Network structure of subway passenger flows

TL;DR

This paper analyzes passenger flow patterns in the Beijing Subway using trip data to construct a directed weighted network, revealing hierarchical and power-law properties that reflect urban mobility and polycentric structure.

Contribution

It introduces a novel passenger flow network model incorporating real trip data, capturing temporal and hierarchical patterns of urban subway mobility.

Findings

Passenger flows follow power-law distributions based on flow size.

Hierarchical clustering reveals urban polycentric structure.

Temporal patterns distinguish in-transit and waiting passengers.

Abstract

The results of transportation infrastructure network analyses have been used to analyze complex networks in a topological context. However, most modeling approaches, including those based on complex network theory, do not fully account for real-life traffic patterns and may provide an incomplete view of network functions. This study utilizes trip data obtained from the Beijing Subway System to characterize individual passenger movement patterns. A directed weighted passenger flow network was constructed from the subway infrastructure network topology by incorporating trip data. The passenger flow networks exhibit several properties that can be characterized by power-law distributions based on flow size, and log-logistic distributions based on the fraction of boarding and departing passengers. The study also characterizes the temporal patterns of in-transit and waiting passengers and provides a hierarchical clustering structure for passenger flows. This hierarchical flow organization varies in the spatial domain. Ten cluster groups were identified, indicating a hierarchical urban polycentric structure composed of large concentrated flows at urban activity centers. These empirical findings provide insights regarding urban human mobility patterns within a large subway network.