# A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management

arXiv: https://arxiv.org/abs/2006.04200 · HTML: https://mlsanity.com/paper/2006.04200/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning](https://mlsanity.com/paper/2311.15341/) (2023)
- [Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems](https://mlsanity.com/paper/2106.08307/) (2021)
- [Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes](https://mlsanity.com/paper/2401.01841/) (2024)
- [Auto311: A Confidence-guided Automated System for Non-emergency Calls](https://mlsanity.com/paper/2312.14185/) (2023)
- [Designing Decision Support Systems for Emergency Response: Challenges and Opportunities](https://mlsanity.com/paper/2202.11268/) (2022)
- [Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search](https://mlsanity.com/paper/2202.13003/) (2022)
- [Artificial Intelligence for Emergency Response](https://mlsanity.com/paper/2306.10068/) (2023)
- [An Emergency Disposal Decision-making Method with Human--Machine Collaboration](https://mlsanity.com/paper/2305.18165/) (2023)
- [Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events](https://mlsanity.com/paper/2402.09553/) (2024)

## Abstract (arXiv)

In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems. The problem has been noted as inherently difficult and constitutes spatio-temporal decision making under uncertainty, which has been addressed in the literature with varying assumptions and approaches. This survey provides a detailed review of these approaches, focusing on the key challenges and issues regarding four sub-processes: (a) incident prediction, (b) incident detection, (c) resource allocation, and (c) computer-aided dispatch for emergency response. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. We conclude by illustrating open challenges and opportunities for future research in this complex domain.