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Authors: Blaž Kažic ; Dunja Mladenić and Aljaž Košmerlj

Affiliation: Jožef Stefan Institute, Slovenia

Keyword(s): Time Series, Traffic Flow Prediction, Feature Engineering, Ridge Regression, SVR, Random Forests.

Abstract: Due to increasing demand and growing cities, traffic prediction has been a topic of interest for many researchers for the past few decades. The availability of large amounts of traffic-related data and the emerging field of machine learning algorithms has led to a significant leap towards data-driven methods. In this paper, loop counter data are used to develop models that can predict traffic flow for several different prediction intervals into the future. In depth exploratory data analysis and statistical testing is performed to obtain good quality informative features. Several feature sets were compared by using different machine learning methods: Ridge Regression, SVR and Random Forests. The results show that in order to obtain good prediction results thorough feature extraction is just as or even more important than learning method selection. Good features enables us to use less complex methods, which run faster, are more reliable and easier to maintain. In conclusion, we address ideas regarding how predictions could be improved even further. (More)

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Paper citation in several formats:
Kažic, B.; Mladenić, D. and Košmerlj, A. (2015). Traffic Flow Prediction from Loop Counter Sensor Data using Machine Learning Methods. In Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS; ISBN 978-989-758-109-0; ISSN 2184-495X, SciTePress, pages 119-127. DOI: 10.5220/0005495001190127

@conference{vehits15,
author={Blaž Kažic. and Dunja Mladenić. and Aljaž Košmerlj.},
title={Traffic Flow Prediction from Loop Counter Sensor Data using Machine Learning Methods},
booktitle={Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS},
year={2015},
pages={119-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005495001190127},
isbn={978-989-758-109-0},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Traffic Flow Prediction from Loop Counter Sensor Data using Machine Learning Methods
SN - 978-989-758-109-0
IS - 2184-495X
AU - Kažic, B.
AU - Mladenić, D.
AU - Košmerlj, A.
PY - 2015
SP - 119
EP - 127
DO - 10.5220/0005495001190127
PB - SciTePress

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