Abstract Urban rail transit (URT) service disruptions, though infrequent, involve high risk and uncertainty, often triggering passenger behaviors that deviate from normal patterns. How stranded passengers make travel decisions under such conditions is essential for improving emergency response and system resilience. However, traditional models based on expected utility theory (EUT) often fail to capture the psychological complexity and bounded rationality exhibited during disruptions. This study develops a decision-making framework that integrates cumulative prospect theory (CPT) with multi-attribute decision-making to model travel mode choices of stranded passengers under non-mandatory evacuation URT service disruption scenarios. Stated-preference (SP) surveys were designed to simulate varied disruption scenarios, capturing passenger evaluations of alternative modes such as detour URT routes, buses, ride-hailing, and cycling. A baseline EUT model and three CPT-based models with different parameter constraints were calibrated using empirical data. Results show that CPT-based models, especially those applying nonlinear value and probability functions to travel time, significantly outperform EUT models in fitting behavioral patterns. Passengers tend to overestimate low-probability events and underestimate high-probability ones, with heightened sensitivity near the extremes of probability. The value function calibration reveals increasing sensitivity to time gains and diminishing sensitivity to losses, reflecting anxiety-driven urgency and “loss numbness”—a sharp reaction to initial delays that flattens as delays grow. The proposed model effectively captures key psychological mechanisms in disruption scenarios, offering a more behaviorally realistic basis for predicting passenger responses. These insights support more targeted emergency resource deployment and behavior-sensitive management strategies, contributing to enhanced resilience in urban rail systems.
Wang et al. (Wed,) studied this question.