Artificial Intelligence for Emergency Response
It addresses the problem of improving emergency response efficiency for communities and first responders, but it is incremental as it synthesizes existing knowledge without introducing new methods.
This tutorial paper tackles the challenge of emergency response management by exploring four sub-problems—incident prediction, detection, resource allocation, and dispatch—and presents mathematical formulations and frameworks for each, along with sharing open-source synthetic data from a U.S. metropolitan area.
Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response.