Evacuation effect analysis of guidance strategies on subway station based on modified cellular automata model

With the extension of the subway network and the increase in passenger flow, higher traffic pressure and risk level have occurred. Adopting appropriate guidance strategies is a critical factor in improving the evacuation effect and reducing casualties in subway stations. Automatic fare gate (AFG) groups are the key facilities in evacuation guidance, as their limited traffic capacity can easily cause congestion. This study proposes a modified cellular automata model that combines the multi-phase goals of passengers in the evacuation process to characterize the behavior of three guidance strategies: (A) Balancing passengers' AFG group selection; (B) Balancing passengers' AFG selection; (C) Setting dedicated AFGs for slower passengers. The pedestrian parameters obtained by field observation are used as inputs for the proposed evacuation model. The total evacuation time and the average waiting time for different types of passengers and key facilities are calculated. The impact of different guidance strategies on evacuation performance is explored in various scenarios. The results show that a combination of A and B strategies would achieve the shortest total evacuation time. While a combination of A, B, and C strategies would achieve the shortest average waiting time. The improvement of evacuation efficiency brought by guidance is proved significant even when only part of the people executes the arrangement.