AI

Scalable evacuation routing in a dynamic environment

Abstract

In emergency management, tools are needed so we can take the appropriate action at different stages of an evacuation. Recent wildfires in California showed how quickly a natural disaster can affect a large geographical area. Natural disasters can create unpredicted traffic congestion or can temporarily block urban or rural roads. Evacuating a large area in an emergency situation is not possible without prior knowledge of the road network and the ability to generate an efficient evacuation plan. An ideal evacuation routing algorithm should be able to generate realistic and efficient routes for each evacuee from the source to the closest shelter. It should also be able to quickly update routes as the road network changes during the evacuation. For example, if a main road is blocked during a flood, the evacuation routing algorithm should update the plan based on this change in the road network. In this article major works in evacuation routing have been studied and a new algorithm is developed that is faster and can generate better evacuation routes. Additionally, it can quickly adjust the routes if changes in the road network are detected. The new algorithm's performance and running time are reported.