An Agent-Based Dynamic Framework for Population Evacuation Management

Evacuating the population during crises to safe zones via optimal paths is vital. The evacuation planning process makes two main decisions: which shelter to reach and which path to take towards the chosen shelter. These decisions correspond to shelter allocation and traffic assignment problems, respectively. Many studies tackled these problems with a static formulation in the literature, while only a few considered a dynamic context. We conduct a comprehensive literature review and highlight that most studies independently solve these two problems while both are correlated with traffic conditions. To fill this gap, we propose a new framework to couple the shelter allocation problem (SAP) and the dynamic traffic assignment (DTA) problem and solve them. To capture traffic dynamics, we use a dynamic agent-based simulator. We assume the system determines the evacuees' shelters to minimize the total evacuation time. However, each evacuee's concern is reaching a shelter as fast as possible. Therefore, we formulate the DTA problem under stochastic user equilibrium (SUE) principles, i.e., every evacuee aims to minimize his own perceived travel time. We apply the proposed methodology to the network of Luxembourg City and compare its performance with other advanced methods that solve SAP and DTA separately. The comparison shows that solving the dynamic shelter allocation improves the mean evacuation time and significantly decreases the network clearance time compared to other methods with a fixed plan for SAP. The simulation results prove that considering the network state in the SAP can provide a more effective evacuation plan. Moreover, we perform a sensitivity analysis on optimization parameters and evaluate the computation cost of our methodology.