Statistical Decision Procedures for Emergency Response
The goal of this project is to improve emergency response systems using proactive resource management that minimizes time and maximizes the effectiveness of the response. With road accidents accounting for 1.25 million deaths globally and 240 million emergency medical services (EMS) calls in the U.S. each year, there is a critical need for a proactive and effective response to these emergencies. Furthermore, a timely response to these incidents is crucial and lifesaving for severe incidents. The process of managing emergencies requires full integration of planning and response data and models and their implementation in a dynamic and uncertain environment to support real-time decisions of dispatching emergency response resources. However, the current state-of-the-art research has mainly focused on advances that target individual aspects of emergency response (e.g., prediction, optimization) when different components of an Emergency Response Management (ERM) system are highly interconnected. Additionally, the current practice of ERM workflow in the U.S. is reactive, resulting in a large variance in response times. Our approach to this problem uses continuous-time generative models to forecast spatiotemporal incidents and the decision-theoretic problem of dispatching responders based on semi-Markovian dynamics. We have also developed efficient and scalable approaches to solve the high-dimensional optimization problem of proactive stationing and dispatch under uncertainty by using Multi-agent Monte Carlo Tree Search (MMCTS). Further, we have developed analysis procedures to help answer questions such as "where should the next fire station be located,'' "how many new emergency dispatch trucks are required'' and reducing the average response time.
- Financial Times: https://www.ft.com/content/140ae3f0-1b6f-11ea-81f0-0c253907d3e0
- Government Technology: https://www.govtech.com/public-safety/Data-Drives-Down-Nashvilles-Emergency-Response-Times.html