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On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities
Emergency Response Management (ERM) is a critical problem faced by commu...
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A Review of Emergency Incident Prediction, Resource Allocation and Dispatch Models
Emergency response to incidents such as roadway accidents is one of the ...
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A Dynamic Simulation-Optimization Model for Adaptive Management of Urban Water Distribution System Contamination Threats
Urban water distribution systems hold a critical and strategic position ...
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Designing Emergency Response Pipelines : Lessons and Challenges
Emergency response to incidents such as accidents, crimes, and fires is ...
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Hierarchical Planning for Resource Allocation in Emergency Response Systems
A classical problem in city-scale cyber-physical systems (CPS) is resour...
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Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning
A crucial and time-sensitive task when any disaster occurs is to rescue ...
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Lost Silence: An emergency response early detection service through continuous processing of telecommunication data streams
Early detection of significant traumatic events, e.g. a terrorist attack...
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An Online Decision-Theoretic Pipeline for Responder Dispatch
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.
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