Abstract:

Stairs are the bottleneck areas in the process of passenger evacuation in the subway station. The safety assessment of passengers passing through the stairs helps to formulate the evacuation plan in advance. Firstly, aiming at the difficulty of collecting the evacuation data of passengers ascending the stairs, MassMotion simulation software was adopted to build a stair scene to simulate the evacuation behavior of passengers ascending the stairs, and the basic data of evacuation time were obtained. Then, the random forest model was trained and tested with basic data to predict the evacuation time of passengers ascending the stairs. Finally, a comprehensive evaluation model of evacuation safety was established, and the evacuation safety level of passengers ascending the stairs in the subway station was evaluated with evacuation time, passenger density and evacuation panic as indicators. The research results indicate that mean absolute error(MAE) of the prediction results of the random forest model used in this paper is 3.45 s, and mean absolute percentage error (MAPE) is 3.8%. Compared with back propagation neural network (BPNN) model and support vector regression (SVR) model, the prediction accuracy is higher. The comprehensive evaluation model of evacuation safety is used to evaluate the safety of the stairs in a subway station in Qingdao, and the evaluation value of evacuation safety in the early peak period is medium.

Key words: subway station, stairs, evacuation time, safety evaluation, random forest model, MassMotion