StatResp – A toolchain for statistical methods in emergency response management
Emergency response management (ERM) is a critical problem faced by communities across the globe. First-responders are constrained by limited resources, and must attend to different types of incidents like traffic accidents, fires, and distress calls. 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. As a consequence, statistical and algorithmic approaches to emergency response have received significant attention in the last few decades. Governments in urban areas are increasingly adopting methods that enable Smart Statistical Emergency Response, which are a combination of forecasting models and visualization tools to understand where and when incidents occur, and optimization approaches to allocate and dispatch responders. Please refer to a preprint of our survey paper for more information.
We are building ‘StatResp’ – an open-source integrated tool-chain to aid first responders understand where and when incidents occur, and how to allocate responders in anticipation of incidents.
Our work in ERM has spanned the last six years. This project was started by a collaboration between the Smart and Resilient Computing for Physical Environments Lab (SCOPE) and the Computational Economics Research Lab (CERL) at Vanderbilt University, and is currently developed jointly by the Stanford Intelligent Systems Lab (SISL) at Stanford and the SCOPE Lab. We are thankful to the Center of Automotive Research at Stanford (CARS), the National Science Foundation (NSF), and Tennessee Department of Transportation (TDOT) for sponsoring the project. We have had the fortune of collaborating with the Tennessee Department of Transportation (TDOT), the Nashville Fire Department (MNPD) and Chattanooga City during this project. Currently, this open-source repository is a collection of forecasting, planning, and operationalization tools. We also collaborate actively with Hemant Purohit from George Mason University to model the dynamics of crowd sourced incident data and use it in the resource allocation models. A key component of this work is a set of open source forecasting, clustering and visualization tools to aid first responders better understand the dynamics of spatial-temporal incident occurrence.
Our research has been showcased at multiple global smart city summits, won an innovation from the government technology magazine, covered in the Financial Times, and won the best paper award at ICLR’s AI for Social Good Workshop. Our broader approach can be understood from checking the Research page, or through the overview paper.