Optimizing how ambulances are allocated and routed is one of the most efficient ways for EMS to save more lives at virtually no cost. However, current EMS software uses models that assume normal demands. These models 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 optimized for normal times may be very inefficient and cause a significant delay in care. This project will synthesize real-time information on case clusters, hospitals’ capacities and capabilities, waiting times, and traffic flows to coordinate optimal responses among ambulances. By design, this system will rapidly adapt to changing situations and disruptions. It will guarantee that ambulances arrive at the scenes as quickly as possible and strategically route patients to care facilities.
Project team: 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)