Subway-Emergency Bridging Strategy for Bus Scheduling Considering Passenger Travel Behavior

Abstract

Unplanned subway interval disruptions caused by equipment failures frequently occur in major cities around the world. It is crucial to ensure that passengers can complete their subsequent travel with less delay. Using bridging buses to evacuate passengers is currently the most common response strategy used by management, but in actual operation there are problems with unclear passenger demand and inefficient connections. To address this gap, we developed a passenger travel behavior decision-making model based on cumulative prospect theory and an emergency bus bridging model. The models serve two primary purposes: (1) to identify the origin–destination of affected passenger flow within the bridging zone during disruptions; and (2) to propose an integrated emergency bus bridging strategy that takes passenger demand into account. The solution efficiency of the proposed strategy is enhanced using a simulated annealing-improved genetic algorithm. We validate the model through a case study of Fuzhou Subway Line 1 in China, followed by a sensitivity analysis examining variables such as the number of emergency buses, passenger demand, subway disruption recovery time, and total passenger travel time. The results indicate that the proposed strategy significantly improves evacuation efficiency, allowing passengers to incur lower travel costs compared to baseline strategies.