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. In prior art, as well as practice, incident forecasting and response are typically siloed by category and department, reducing effectiveness of prediction and precluding efficient coordination of resources. Further, most of these approaches are offline and fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Consider the classical problem of emergency response. The goal of responders is to minimize the variance in the operational delay between the time incidents are reported and when responders arrive on the scene.
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. Now, consider solving this problem when there is a high uncertainty in the veracity of the request due to either communication failures or due to the nature of the communication medium – in extreme disruptions the most common communication mechanism used is social media, however, the social media requests have a lot of uncertainty in terms of duplication, spatial location etc. Ultimately, the methods developed in this work can be applied to other domains where multi-resource spatio-temporal scheduling is a challenge. We collaborate with Prof. Yevgeniy Vorobeychik, WUSTL, Prof. Hemant Purohit, GMU and Prof. Saideep Nannapaneni, Wichita State.
This project is funded in part by the National Science Foundation.