Accepted paper at ICDM 2022 Workshop UNIT 2022

Authors

Niklas Strauss, Max Berrendorf, Tom Haider, Matthias Schubert

The 1st Workshop on Urban Internet-of-Things Intelligence (UNIT 2022) co-located with the 22nd IEEE International Conference on Data Mining (ICDM 2022),
28 November 2022, Orlando, FL, USA

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.

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