Narrow Results

Passenger evacuation time prediction is a crucial basis for subway station management to better formulate safety control measures. It becomes possible to reasonably plan the flow of passengers within the station and configure safety devices such as signage and sprinkler systems with a known and explicit time framework, thereby reducing passenger congestion and panic. However, methods based on crowd dynamics simulation require a significant amount of time and effort to build models, and the rapidity of time prediction is challenging to ensure. Real human evacuation experiments involve ethical, safety and practical operational issues. To address this challenge, an evacuation time prediction model for subway passengers is established based on the CPA-SVR machine learning method, enhancing the speed and accuracy of prediction. The reliability of simulation results is validated by comparing observed values of passenger alighting and boarding time and traffic time at stairs with simulation values from MassMotion software. Fourteen factors related to the subway station structure, passengers and train status are selected as influence factors for evacuation time. A foundation data set for the evacuation time prediction model is obtained through 179 evacuation experiments under different influence factors using the MassMotion simulation system at 32 constructed stations. The SHAP interpretation method is applied to identify the importance of influence factors in the experimental results. A CPA-SVR passenger evacuation time prediction model is established, with accuracy concentrated between 85%–100%, based on training and validation sets. Further testing with 45 additional sets of fresh experimental data demonstrates the model’s strong predictive capability for new data, indicating good generalization ability. Finally, a focused analysis of passenger evacuation behaviors at bottlenecks such as stairs, gates and exits is conducted, accompanied by relevant optimization strategies.

Pedestrian heterogeneity is one of the important factors affecting evacuation efficiency in subway stations. This paper mainly studies the impact of pedestrian heterogeneity on evacuation based on simulations. With the help of Massmotion, the Qingdao Jinggangshan Road subway station is modeled. The social force model is used as the pedestrian dynamics model and the minimum cost model is used as the decision-making mechanism of pedestrian path selection. The models are verified by comparing the field data with the corresponding simulation data. Fully considering the impact of different pedestrian attributes on evacuation efficiency, pedestrians are divided into three categories with different speed levels and three categories with different body size levels. Simulation experiments are carried out by adjusting the proportional relationship of the number of pedestrians with different attributes. The simulation results indicate that the larger the proportion of fast pedestrians under the same number of evacuees, the higher the evacuation efficiency to a certain extent. The evacuation efficiency could be reduced accordingly with the increase in the proportion of pedestrians with large body sizes. When the pedestrian density is large, the impact of pedestrian heterogeneity on evacuation cannot be clearly reflected. Moreover, the quantitative fitting relationship between evacuation time and pedestrian quantity could be obtained. This paper provides a theoretical basis for the determination of evacuation strategy for the heterogeneous crowd.