Optimizing ambulance allocation and routing is one of the most efficient ways for EMS to save more lives at virtually no cost. However, current EMS software was 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, emergency responders would like to synthesize real-time information on case clusters, hospital capacity, wait times, and traffic situations 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 can rapidly adapt to changing situations and is robust to disruptions. It guarantees that ambulances arrive at scenes the fastest and distribute patients optimally among care facilities. The research team has strong ties with technical personnel of the Austin Travis County EMS. This will allow them to obtain high-quality real data and consult EMS dispatchers for feedback. The team expects to run real-life field test of the algorithms on ATCEMS within six months of the project initiation.
Tran Mai Ngoc (Department of Mathematics), Evdokia Nikolova (Department of Electrical and Computer Engineering), David Kulpanowski (City of Austin, Austin Travis County EMS Department), George Torres (Department of Mathematics), Ali Khodabakhsh (Department of Electrical and Computer Engineering) and Yutong Wu (Department of Electrical and Computer Engineering)