Vulnerability Evaluation and Critical Turn-Back Interval Identification for an Urban Rail Transit Network

Abstract It is important to accurately assess the vulnerability of Urban Rail Transit (URT) networks and identify the critical intervals whose interruptions highly impact URT network operation. Existing studies overlook the impact of Turn-Back (T-B) train operations and lack a multi-objective optimization model to identify critical intervals affecting network vulnerability. In this context, the concept of train T-B operation is analyzed, where the segment between two successive T-B stations is delineated as a T-B interval for evaluating the vulnerability of a URT network. Three indicators are proposed from network structural and service perspectives to comprehensively assess the vulnerability of a URT network. A multi-objective optimization model is proposed for identifying critical T-B intervals and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is employed to solve the model. The proposed model and solution algorithm are applied to the Wuhan metro network. Results show that the Wuhan metro network exhibits greater vulnerability from a structural standpoint compared to its vulnerability from a service perspective. Most of the critical T-B intervals belong to Line 2, indicating its pivotal role in this network. The most critical T-B intervals typically have high passenger volumes and connect transfer stations. This study can assist operators in estimating the impact of network interruptions and offer decision support for planning effective maintenance strategies for critical T-B intervals within a URT network.