Agent-Based Simulation Model for Rescuing Operations in Crowd Mass Disasters: Application to the Old City of Jerusalem

Crowd mass disasters occur over a relatively short time, and rescue operations in disasters, such as earthquakes, are challenging because of people’s behavior, type, or location. Therefore, it is essential to devise means and methods to manage such problems to minimize the consequences as much as possible. During disasters, rescue operations should be conducted in a timely conducted to save people’s lives. Otherwise, losses and consequences are severe, and if there are no proper rescuing operation models, the situation worsens, and the consequences are devastating. In particular, the allocation and coordination of limited rescue resources have a critical impact on response times and the number of lives saved. This paper aims to develop an Agent-Based Simulation (ABS) model for rescuing operations in crowd-mass disasters with six main intelligent agents. The proposed model explicitly represents the interactions among victims, rescuers, command-and-control entities, transportation assets, road networks, and affected infrastructure within a GIS-based urban environment. The developed model is based on an enhanced approach to improve rescue agents’ tasks allocation operations that enable modeling and simulation to make critical decisions for people to be rescued in a crowded mass disaster. Our task-allocation mechanism incorporates dynamic accessibility of roads, time-dependent rescue capacity, and context-aware prioritization of victims. Three related task-allocation strategies from the literature are used as baselines under identical scenarios, and performance is compared in terms of average rescue time and number of rescued victims. Results show that the proposed model achieves more efficient and robust rescue operations in most simulated experiments.