Optimization of Emergency Evacuation Route Based on Ripple-spreading Algorithm

This study proposes a capacity constrained ripple spreading algorithm (CCRSA) aimed at optimizing emergency evacuation routes in expansive public spaces during crowd emergencies. This algorithm dynamically updates the remaining maximum traffic capacity of each link at each moment, and adds ripple waiting behavior at nodes when the capacity is insufficient. It identifies the shortest evacuation path considering waiting times from multiple starting points to various destinations simultaneously. Subsequently, path optimization rules determine priority evacuation routes and allocate the number of evacuees, thereby achieving differentiated evacuation and enhancing the utilization of different routes within the road network. The algorithm is tested across numerous randomized road networks featuring different number of nodes and evacuees, and an actual road network scenario at the Summer Palace in Beijing. Three evaluation criteria, evacuation time, standard deviation between actual and ideal evacuation times per individual, and program running time, are established. The experimental findings demonstrate that CCRSA, in comparison to conventional emergency evacuation path planning algorithms, reduces evacuation time by an average of 13.07% and generates evacuation plans that better align with the expectations of evacuees while exhibiting enhanced program efficiency. © 2024 Science Press. All rights reserved.