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

TL;DR

This survey reviews statistical, data-driven, and algorithmic models for emergency management, focusing on incident prediction, detection, resource allocation, and dispatch, highlighting challenges and future research directions.

Contribution

It provides a comprehensive comparison of modeling approaches for emergency response, identifying strengths, weaknesses, and open challenges in the field.

Findings

Different modeling paradigms have varying strengths and weaknesses.

Key challenges include uncertainty and spatio-temporal decision making.

Future research opportunities involve integrating new data sources and algorithms.

Abstract

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.