Improving the dispatch response strategies for cities using predictive models and machine learning

What is it about?

The objective of this research is to understand and improve the resource coordination and dispatch mechanisms used by first responders. The problem of dispatching emergency responders to service accidents, fire, distress calls and crimes plagues urban areas across the globe. Solving this problem requires not just sending the nearest emergency responder, but sometimes being proactive placing emergency vehicles in regions with higher incident likelihood. Sending the nearest available responder by euclidean distance ignores road networks and their congestion, as well as where the resources are stationed. Greedily assigning resources to incidents can lead to resources being pulled away from their stations, increasing response times if an incident occurs in the future in the area where responder should be positioned. This paper describes a methodology to determine the dispatch policy in real-time.

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