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Authors: Matthias Sommer and Jörg Hähner

Affiliation: University of Augsburg, Germany

ISBN: 978-989-758-242-4

Keyword(s): Road Traffic, Congestion Detection, Learning Classifier Systems, Machine Learning, Reinforcement Learning.

Abstract: The increase in mobility leads to a higher number of kilometres driven per vehicle and more delay due to congestion which poses a recent and future problem. Congestion generates growing environmental pollution and more car accidents. We apply machine learning concepts to the task of congestion detection in road traffic. We focus on the extended classifier system XCSR, an evolutionary rule-based on-line learning classifier system. Experiments with real-world detector data demonstrate high accuracy of XCSR for congestion detection on interstates.

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Paper citation in several formats:
Sommer M. and Hähner J. (2017). Learning Classifier Systems for Road Traffic Congestion Detection.In Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-242-4, pages 142-150. DOI: 10.5220/0006214101420150

@conference{vehits17,
author={Matthias Sommer and Jörg Hähner},
title={Learning Classifier Systems for Road Traffic Congestion Detection},
booktitle={Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2017},
pages={142-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006214101420150},
isbn={978-989-758-242-4},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Learning Classifier Systems for Road Traffic Congestion Detection
SN - 978-989-758-242-4
AU - Sommer M.
AU - Hähner J.
PY - 2017
SP - 142
EP - 150
DO - 10.5220/0006214101420150

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