Robust optimization modelling of passenger evacuation control in urban rail transit for uncertain and sudden passenger surge

Abstract

ABSTRACT Effectively addressing the surge in passenger flow caused by emergencies represents a critical challenge in the emergency management of urban rail transit operations. This article introduces a comprehensive control strategy for passenger evacuation, employing robust optimization methods to tackle the uncertainties associated with sudden increases in passenger numbers. Initially, a comprehensive mathematical optimization model for train scheduling and passenger flow coordination is established to ensure efficient passenger transport while maximizing the reduction of operational costs for the operating company’s emergency response. Subsequently, the impact of uncertainty factors on the evacuation model is considered. The fluctuation in passenger flow is represented using a range of intervals, and a robust corresponding model is formulated by introducing an uncertainty budget coefficient. Furthermore, small-scale numerical examples are utilized to discuss passenger evacuation plans, conduct robustness analysis, and assess demand sensitivity. The practical case of the Beijing subway demonstrates that, in contrast to the most optimistic scenario, the robust passenger plan, accounting for a 1% fluctuation in passenger flow, exhibits a reduced cost increase from 10.96% to 9.13%, as compared to the most conservative situation.These findings contribute to enhancing the emergency response capabilities of operational management departments.