Emergency response systems must operate under conditions of uncertainty and incomplete information while coordinating many heterogeneous agencies and data sources. This work is innovative because it provides a principled approach that integrates multiple technical challenges including data integration, learning under sparsity, and decentralized decision-making. The authors address the fundamental tension between needing accurate predictions and having limited incident data.
This paper presents a comprehensive framework for designing emergency response management systems, addressing the challenges of coordinating multiple agencies, collecting diverse geospatial data, and providing incident forecasting and resource allocation. The system integrates data curation components, incident detection models, and dynamic resource allocation algorithms while emphasizing how to handle sparse and uncertain incident data across large geographic areas.
The framework was evaluated on real incident data from Tennessee highways and demonstrated superior performance compared to existing approaches. Key findings show that incident prediction models benefit significantly from geographic and temporal feature engineering, and that resource allocation must adapt to non-stationary incident patterns. The system successfully integrated sparse incident data with weather and traffic information to improve emergency response times.
@inproceedings{pettet2022designing,
author = {Pettet, G. and Baxter, H. and Vazirizade, S. and Purohit, H. and Ma, M. and Mukhopadhyay, A. and Dubey, A.},
booktitle = {2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER)},
title = {Designing Decision Support Systems for Emergency Response: Challenges and Opportunities},
year = {2022},
address = {Los Alamitos, CA, USA},
month = {may},
pages = {30-35},
publisher = {IEEE Computer Society},
abstract = {Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners.},
contribution = {lead},
doi = {10.1109/CPS-ER56134.2022.00012},
keywords = {emergency response, resource allocation, incident detection, incident forecasting, cyber-physical systems},
url = {https://doi.ieeecomputersociety.org/10.1109/CPS-ER56134.2022.00012},
month_numeric = {5}
}