An efficient evacuation path optimization for passengers in subway stations under floods

Abstract

The optimization of passenger evacuation paths in subway stations under flood scenarios plays an important role in improving evacuation efficiency and ensuring escape safety. Considering the impact of evacuation network failures such as gates on path planning, a two-stage passenger evacuation path optimization method under flood scenarios of subway stations is established with the objectives of minimizing total evacuation time, risk, and congestion. The black widow algorithm is proposed to optimize the BP neural network model to predict the travel time of passengers at nodes, which could improve the prediction accuracy of calculating the total evacuation time. The path optimization model is solved by the NSGA-II algorithm, and the optimal Pareto solution is determined based on the minimum total cost. Taking a subway station in Qingdao, China as an example, a passenger evacuation simulation system under flood scenarios is built using PathFinder software. The effect of the path optimization strategy is comprehensively evaluated through comparative experiments. It is found that the overall evacuation optimization degree could be increased by 18.75%, by comparing the evacuation time, congestion, and risk objectives under the situations with and without path optimization strategies.