Emergency response systems face complex challenges in routing limited resources to incidents in dynamic urban environments. Traditional systems dispatch the nearest responder, which ignores future incident probabilities and environmental factors. This paper addresses these limitations through a principled decision-theoretic approach that integrates incident prediction with dynamic dispatch optimization.
This paper presents an online decision-theoretic pipeline for responder dispatch in emergency management systems. The work formulates the responder dispatch problem as a Semi-Markov Decision Process and develops an online incident prediction model based on survival analysis to enable real-time, data-driven dispatch decisions.
The paper demonstrates the effectiveness of the approach through evaluation on real emergency services data from Nashville, Tennessee. The online prediction and dispatch pipeline reduces response times compared to baseline approaches while accounting for incident cascading effects and changing environmental dynamics. The work successfully bridges incident prediction and optimal dispatch decisions.
@inproceedings{Mukhopadhyay2019,
author = {Mukhopadhyay, Ayan and Pettet, Geoffrey and Samal, Chinmaya and Dubey, Abhishek and Vorobeychik, Yevgeniy},
booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
title = {An online decision-theoretic pipeline for responder dispatch},
year = {2019},
pages = {185--196},
abstract = {The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/iccps/MukhopadhyayPSD19},
category = {selectiveconference},
contribution = {lead},
acceptance = {27},
doi = {10.1145/3302509.3311055},
file = {:Mukhopadhyay2019-An_Online_Decision_Theoretic_Pipeline_for_Responder_Dispatch.pdf:PDF},
keywords = {emergency response, responder dispatch, decision-theoretic planning, SMDP, incident prediction, survival analysis},
project = {smart-cities,smart-emergency-response},
tag = {ai4cps,incident},
timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
url = {https://doi.org/10.1145/3302509.3311055}
}