Under degraded operations in urban rail transit systems, the coordinated state between passengers and trains undergoes significant changes due to factors such as train frequency reduction. This creates a critical mismatch between passenger demand and transport capacity, leading to rapid passenger accumulation at stations and potential safety hazards from abnormal crowding and systemic congestion. This study develops an integrated simulation model that combines train schedules with passenger arrival patterns to quantify how altered departure sequences generate supply-demand imbalances. A bounded rationality route choice model incorporating four decision-making dimensions – time value, transfer burden, service degradation impact, and route familiarity – captures passenger behavioral adaptations during degraded operations. The experimental results indicate that under abnormal train operation scenarios, the model incorporating bounded rationality assumptions is closer to actual observed values compared with the fully rational benchmark model. In the L2 degradation scenario, the deviation between the bounded rationality model and the actual values is controlled within 0.6%–1.3%, whereas the deviation of the fully rational model is approximately 12%. Concurrently, passenger flow propagation intensity in core sections rises from 0.3 to 0.7, showing a clear evolution from localized accumulation to regional diffusion. The research provides an effective methodology for identifying passenger flow dynamics under degraded operational scenarios and supports decision-making for service optimization and passenger flow management.
Zuo et al. (Mon,) studied this question.