Learning Classifier Systems for Road Traffic Congestion Detection

Matthias Sommer, Jörg Hähner

2017

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.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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