# Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems

arXiv: https://arxiv.org/abs/2106.08307 · HTML: https://mlsanity.com/paper/2106.08307/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 15 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)

## Built on by (most cited first, 5 of 5)

- [Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing](https://mlsanity.com/paper/2405.13205/) (2024)
- [Designing Decision Support Systems for Emergency Response: Challenges and Opportunities](https://mlsanity.com/paper/2202.11268/) (2022)
- [Artificial Intelligence for Emergency Response](https://mlsanity.com/paper/2306.10068/) (2023)
- [Decision-making of Emergent Incident based on P-MADDPG](https://mlsanity.com/paper/2203.12673/) (2022)
- [Forecasting and Mitigating Disruptions in Public Bus Transit Services](https://mlsanity.com/paper/2403.04072/) (2024)

## Abstract (arXiv)

Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders.