An Online Decision-Theoretic Pipeline for Responder Dispatch
Ayan Mukhopadhyay, Geoffrey Pettet, Chinmaya Samal, Abhishek, Dubey, Yevgeniy Vorobeychik

TL;DR
This paper introduces an online, decision-theoretic system for emergency responder dispatch that adapts in real-time, improving response times and computational efficiency by integrating incident prediction and environmental modeling.
Contribution
It presents a novel online framework combining decision-theoretic dispatch, incident prediction, and environmental modeling using neural networks, advancing beyond offline methods.
Findings
Reduces emergency response times significantly.
Achieves drastic reduction in computational time.
Outperforms prior state-of-the-art dispatch strategies.
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…
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