Dynamic planning of crowd evacuation path for metro station based on Dynamic Avoid Smoke A-Star algorithm

Abstract

Considerable casualties can easily be caused once a fire occurs in a metro station, and a safe and effective evacuation path in time should be provided, taking on critical significance in the rapid evacuation of the crowd. In existing research, crowd evacuation paths have been generally planned without considering the fire environment and the real-time effect of fire products on the evacuation paths, such that the planned paths do not fit the realistic environment of crowd evacuation. An algorithm is proposed in this study to dynamically plan evacuation paths in fire scenarios in metro stations. First, the equivalent length of the path under the effect of fire on evacuation speed is determined in accordance with the effects of ambient temperature, visibility, and CO concentration, and then the fire risk model is built. Second, the fire risk model is incorporated into the A-Star algorithm, and the evaluation function of the A-Star algorithm is optimized, such that the determined path is capable of avoiding areas with higher fire risk. Subsequently, a dynamic update mechanism considering time factor is introduced to update the search environment information data of the algorithm in real time for dynamic path planning, with the aim of coping with the dynamically changing fire environment. Lastly, the A-Star algorithm is optimized, and the Dynamic Avoid-Smoke A-Star (DASA-Star) algorithm is built. As indicated by the simulation results, the DASA-Star algorithm is capable of making a trade-off between the fire risk and geometric length of the path in an underground station fire scenario and achieving dynamic planning of evacuation paths based on FDS fire simulation results. Accordingly, the algorithm conforms to the real-time requirements of path planning under fire conditions, and it is capable of more effectively planning the optimal evacuation path under the effect of fire.