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

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 is 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 three subprocesses that are part of this problem (a) incident prediction, (b) resource allocation, and (c) computer-aided dispatch to handle the emergency conditions. 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 remain challenges and opportunities for future research in this complex domain.