Simulation of luggage-laden passengers’ behavior in the evacuation process based on a floor field CA model case study: Tehran metro-rail transfer corridor

Abstract

Passengers’ safety against unexpected incidents such as rail stations’ fire accidents is essential in the safety field. The presence of luggage with passengers occupies extra space, diminishes passenger’s velocity in high densities, and consequently increases the evacuation time. Therefore, studying the mixture of luggage-laden passengers with non-luggage-laden passengers during the emergency evacuation time of a rail station is vital. In this paper, a simulation of a metro-rail transfer station using an extended cellular automata (CA) model is used to illustrate the importance of this consideration. In this model, luggage-laden passengers and non-luggage-laden passengers are defined as two-cell and one-cell groups, respectively. Specific parameters for luggage-laden passengers in minimum wall prevention and velocity are used. Also, the volume of each passenger group is extracted from the Wi-Fi scanners during the busiest time of the normal station operational hours due to metro and railway schedules. The simulation is carried out using the Python programming language. Fourteen scenarios that vary in their impact on the three classifications of station infrastructure, station equipment, and management’s approach are presented. The analysis indicates that approximately 28% of passengers, or 236 passengers, will not be evacuated in the time period predicted by the simulation if the luggage is not considered. Interestingly, resizing retail stores in the corridor reduced emergency evacuation times by 6.3%, the equivalent of removing them. Failures in the two escalators affect an 8% and 9.4% increase in emergency evacuation time and cause 28 and 46 more passengers to be trapped, respectively. Although the construction of the second railway entrance corridor has been suspended, results indicate that it will save 67 passengers and reduce evacuation time by 9.5%.