Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

1-2015

Abstract

In emergency medical systems, arriving at the incident location a few seconds early can save a human life. Thus, this paper is motivated by the need to reduce the response time – time taken to arrive at the incident location after receiving the emergency call – of Emergency Response Vehicles, ERVs (ex: ambulances, fire rescue vehicles) for as many requests as possible. We expect to achieve this primarily by positioning the "right" number of ERVs at the "right" places and at the "right" times. Given the exponentially large action space (with respect to number of ERVs and their placement) and the stochasticity in location and timing of emergency incidents, this problem is computationally challenging. To that end, our contributions building on existing data-driven approaches are three fold. Finally, we provide an exhaustive evaluation on real-world datasets from two asian cities that demonstrates the improvement provided by our approach over current practice and the best known approach from literature.

Discipline

Computer Sciences | Health and Medical Administration | Operations Research, Systems Engineering and Industrial Engineering

Publication

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence: 25-30 January 2015, Austin, Texas

First Page

702

Last Page

708

ISBN

9781577356981

Publisher

AAAI Press

City or Country

Palo Alto, CA

Copyright Owner and License

LARC

Additional URL

https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9427/9316

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