DEAR: Dynamic Electric Ambulance Redeployment

Rottkamp, Lukas ; Strauß, Niklas und Schubert, Matthias (2023): DEAR: Dynamic Electric Ambulance Redeployment. 18th International Symposium on Spatial and Temporal Data (SSTD), Calgary, Canada, 23. - 25. August 2023. In: Proceedings of the 18th International Symposium on Spatial and Temporal Data, ACM Other conferences ; ACM Digital Library New York: Association for Computing Machinery. S. 11-20

Abstract

Dynamic Ambulance Redeployment (DAR) is the task of dynamically assigning ambulances after incidents to base stations to minimize future response times. Though DAR has attracted considerable attention from the research community, existing solutions do not consider using electric ambulances despite the global shift towards electric mobility. In this paper, we are the first to examine the impact of electric ambulances and their required downtime for recharging to DAR and demonstrate that using policies for conventional vehicles can lead to a significant increase in either the number of required ambulances or in the response time to emergencies. Therefore, we propose a new redeployment policy that considers the remaining energy levels, the recharging stations’ locations, and the required recharging time. Our new method is based on minimizing energy deficits (MED) and can provide well-performing redeployment decisions in the novel Dynamic Electric Ambulance Redeployment problem (DEAR). We evaluate MED on a simulation using real-world emergency data from the city of San Francisco and show that MED can provide the required service level without additional ambulances in most cases. For DEAR, MED outperforms various established state-of-the-art solutions for conventional DAR and straightforward solutions to this setting.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 979-8-4007-0899-2
• Ort: New York
• Sprache: Englisch