Optimizing ambulance allocation and routing is one of the most efficient ways for the EMS to save more lives at virtually no cost. However, current EMS software were developed under models that assume normal demands. They are unable to adapt to disasters such as the COVID-19 pandemic, where traffic patterns change, case clusters emerge, and hospitals rapidly reach capacity. Decisions which are optimized for normal times can suddenly become very inefficient, leading to significant delay in care. Ideally, one would like to synthesize real-time information on case clusters, hospitals’ capacities and capabilities, waiting times, and traffic situation to coordinate responses between all ambulances. This project aims to create such an optimal EMS routing strategy using real-time information. By design, the proposed system aims to rapidly adapt to changing situations and is robust to disruptions. It can guarantee that ambulances arrive at scenes the fastest and distribute patients optimally among care facilities. In this presentation, we report on recent findings of Austin-Travis County EMS incidents, the effects of COVID on EMS demands and system operations and comparisons to state-of-the-art routing algorithms.
Speaker: Ngoc Mai Tran, Yangxunyu Xie, Joshua Ong
Ngoc Tran is an Assistant Professor in the Department of Mathematics at the University of Texas at Austin. She is a data-driven mathematician whose research lies at the intersection of tropical geometry, probability, combinatorics, economics and neuroscience. She is also the PI for the Good Systems Project, “Optimize EMS Responses During Extreme Events” with is a interdisciplinary and transdisciplinary project in collaboration with the City of Austin. This project will create a routing strategy using real-time information that rapidly adapts to changing situations and is robust to disruptions. It will guarantee that ambulances arrive at scenes the fastest and distribute patients optimally among care facilities. This research would be valuable at any time but is especially relevant in the time of COVID.
