Artificial Intelligence for Emergency Response

TL;DR

This paper reviews data-driven models and mathematical frameworks for emergency response management, focusing on incident prediction, detection, resource allocation, and dispatch, and provides open-source data for future research.

Contribution

It introduces broad frameworks and mathematical formulations for key emergency response sub-problems and shares open-source synthetic data for advancing data-driven solutions.

Findings

Frameworks for incident prediction and detection

Mathematical models for resource allocation and dispatch

Open-source synthetic data for emergency response research

Abstract

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.