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

Ayan Mukhopadhyay, Geoffrey Pettet, Sayyed Mohsen Vazirizade, Di Lu, Alejandro Jaimes, Said El Said, Hiba Baroud, Yevgeniy Vorobeychik, Mykel Kochenderfer, Abhishek Dubey

Accident Analysis & Prevention 2022

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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.

Cite This Paper

@article{mukhopadhyay2021review,
  author = {Mukhopadhyay, Ayan and Pettet, Geoffrey and Vazirizade, Sayyed Mohsen and Lu, Di and Jaimes, Alejandro and Said, Said El and Baroud, Hiba and Vorobeychik, Yevgeniy and Kochenderfer, Mykel and Dubey, Abhishek},
  journal = {Accident Analysis & Prevention},
  title = {A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management},
  year = {2022},
  issn = {0001-4575},
  pages = {106501},
  volume = {165},
  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.},
  contribution = {lead},
  doi = {https://doi.org/10.1016/j.aap.2021.106501},
  keywords = {Resource allocation for smart cities, Incident prediction, Computer aided dispatch, Decision making under uncertainty, Accident analysis, Emergency response},
  preprint = {https://arxiv.org/abs/2006.04200},
  url = {https://www.sciencedirect.com/science/article/pii/S0001457521005327}
}
Quick Info
Year 2022
DOI https://doi.org/10.1016/j.a...
Keywords
Resource allocation for smart cities Incident prediction Computer aided dispatch Decision making under uncertainty Accident analysis Emergency response
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Review, Incident, Prediction, Resource, Allocation, Dispatch, Models, Emergency, Management, Resource allocation for smart cities, Incident prediction, Computer aided dispatch, Decision making under uncertainty, Accident analysis, Emergency response, 2022, Mukhopadhyay, Pettet, Vazirizade, Lu, Jaimes, Said, Baroud, Vorobeychik, Kochenderfer, Dubey