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A Comparison of Ambulance Redeployment Systems on Real-World Data

MCML Authors

Link to Profile Matthias Schubert PI Matchmaking

Matthias Schubert

Prof. Dr.

Principal Investigator

Spatial Artificial Intelligence

Abstract

Modern Emergency Medical Services (EMS) benefit from real-time sensor information in various ways as they provide up-to-date location information and help assess current local emergency risks. A critical part of EMS is dynamic ambulance redeployment, i.e., the task of assigning idle ambulances to base stations throughout a community. Although there has been a considerable effort on methods to optimize emergency response systems, a comparison of proposed methods is generally difficult as reported results are mostly based on artificial and proprietary test beds. In this paper, we present a benchmark simulation environment for dynamic ambulance redeployment based on real emergency data from the city of San Francisco. Our proposed simulation environment is highly scalable and is compatible with modern reinforcement learning frameworks. We provide a comparative study of several state-of-the-art methods for various metrics. Results indicate that even simple baseline algorithms can perform considerably well in close-to-realistic settings.

inproceedings

Workshop @ICDM 2022

Workshop at the 22nd IEEE International Conference on Data Mining. Orlando, FL, USA, Nov 30-Dec 02, 2022.

Authors

N. Strauß • M. Berrendorf • T. Haider • M. Schubert

Links

DOI GitHub

Research Area

 A3 | Computational Models

BibTeXKey: SBH+22

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