An Online Decision-Theoretic Pipeline for Responder Dispatch

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 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.