A Framework for Evaluating the Resilience of Transportation Networks: An Optimization Perspective

The resilience of a transportation system is its ability to recover and provide timely services during traffic emergencies, which is crucial for highly urbanized societies. Traffic evacuation efficiency plays a significant role in improving resilience. However, previous studies have not considered maximizing resilience through a multidimensional approach to the siting of disaster response nodes, emergency evacuation, and resource allocation during emergencies. In this paper, we propose a two-stage optimization framework for evaluating the resilience of transportation systems, considering the uncertainty of the level of disaster. In the first stage. A hybrid algorithm combining discrete differential evolution and genetic algorithm was designed (H-DEGA). In the second stage, we calculate the optimal evacuation strategy of the system by continuously removing the important road sections in the traffic network using an optimization model. The system's resilience is derived by assessing its performance before and after disasters. Computational results for the Xi'an city network demonstrate that the model can determine the optimal recovery solution and improve system resilience. Finally, this method provides insights into traffic evacuation and intelligent city management.