Assessing the dynamic vulnerability of an urban rail transit system and a case study of Beijing, China

TL;DR

This paper introduces a novel method to assess the dynamic vulnerability of urban rail systems by considering static topology and fluctuating passenger demand, demonstrated through a case study of Beijing's transit network.

Contribution

It develops a new dynamic vulnerability evaluation index and an accessibility-based method to identify critical stations considering time-varying demand and network topology.

Findings

High-importance stations vary with time of day.

Operational delays impact network vulnerability differently based on passenger flow.

Bridge stations with high passenger flow significantly influence system vulnerability.

Abstract

Urban rail transit is the backbone of urban transportation, and thus it is significant to understand its vulnerability, i.e., whether the system can still maintain normal operations when facing operational disturbances with different magnitudes. To this end, this paper proposes a network vulnerability assessment method with the joint consideration of static network topology and dynamic travel demand. The method includes an accessibility-based identification of station importance with time-varying passenger demand and a new dynamic vulnerability evaluation index. An empirical analysis is carried out by taking the rail transit system of Beijing, China as an example. Results show that the distribution of high-importance stations varies with the time of day, affected by both static topology and hourly-changing passenger flow. Under the disturbance of operation delay, the impact of high-importance stations on the network vulnerability changes with the increase of delayed travel demand. It is also found that some stations that serve as bridges (i.e., reasonable paths link the origin station and destination) and are visited by large passenger flow have the greatest impact on network vulnerability. Network performance degradation is obviously segmented and stratified in the case of interval continuous failure. The disruption between different lines is the main reason for system performance degradation, and some important stations within the line will act as catalysts to accelerate the performance degradation. This method provides a reference for measuring dynamic passenger flow-related network vulnerability and supplies the field with a new vulnerability evaluation index.