A day-to-day evacuation model to maximise transport resilience under dynamic hazards

A transport network may face damage due to a disaster. Some roads may be wholly or partially closed, and the system cannot satisfy the whole demand. Critical considerations include transferring evacuees from dangerous zones to safe zones. This paper presents a novel optimization method that will allow a transport network to run more efficiently during a dynamic hazard that will change through the periods. The objective is to minimize the maximum time needed to evacuate the last group of people from critical and intermediate zones. Regarding the complexity class of evacuation problems, a Genetic Algorithm (GA) approach is designed to solve large-size problems. Also, the Sioux Falls network and Dublin Transportation Network case studies are defined to validate the proposed model and GA approach. This study assesses the system's resilience during a critical event by comparing the system's behavior before and during the hazard, which helps improve the recovery process.