Improving a Maintenance Priority Index for Sustainable Urban Railway Track Management Using Facility Performance Evaluation Data

Abstract

Sustainable urban rail management requires directing limited budgets to sections where maintenance can most effectively extend asset life. Existing track performance evaluation systems guide maintenance priorities, but cost-effectiveness-based indices do not explicitly account for structural vulnerability. Using 4737 unit sections (462.1 km across eight urban railway lines), this study identified factors associated with low performance and developed an improved maintenance priority index. The lowest-performing 25% for each index served as the priority management group. Binary and ordinal logistic regression, with 5–30% cutoff sensitivity analyses, assessed associations with cumulative gross tonnage, trackbed type, and alignment condition. For the overall performance index, ballasted track (odds ratio = 2.17) and sharp curves (odds ratio = 2.38) were consistently associated with priority-group membership across cutoff and spatial-dependence checks. Predicted probabilities from the overall-performance model were converted to ratios relative to a baseline and applied to the existing index as structural-vulnerability weights. In the case application, weights reached 4.03 and changed 23 of 27 section rankings. By making structural vulnerability explicit, the index supports more transparent and resource-efficient maintenance prioritization under constrained budgets, longer component service life, and fewer premature renewals. Longitudinal data are required to test whether these rankings predict subsequent deterioration.