# Artificial Intelligence for Emergency Response

arXiv: https://arxiv.org/abs/2306.10068 · HTML: https://mlsanity.com/paper/2306.10068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Cross-media Structured Common Space for Multimedia Event Extraction](https://mlsanity.com/paper/2005.02472/) (2020)
- [A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management](https://mlsanity.com/paper/2006.04200/) (2020)
- [Hierarchical Planning for Resource Allocation in Emergency Response Systems](https://mlsanity.com/paper/2012.13300/) (2020)
- [An Online Decision-Theoretic Pipeline for Responder Dispatch](https://mlsanity.com/paper/1902.08274/) (2019)
- [On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities](https://mlsanity.com/paper/2001.07362/) (2020)
- [Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems](https://mlsanity.com/paper/2106.08307/) (2021)
- [Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services](https://mlsanity.com/paper/2112.02012/) (2021)

## Built on by (most cited first, 1 of 1)

- [Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events](https://mlsanity.com/paper/2402.09553/) (2024)

## Abstract (arXiv)

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.