Traffic Stream Short-term State Prediction using Machine Learning Techniques

Mohammed Elhenawy, Hesham Rakha, Hao Chen

Abstract

The paper addresses the problem of stretch wide short-term prediction of traffic stream state. The problem is a multivariate problem where the responses are the speeds or flows on different road segments at different time horizons. Recognizing that short-term traffic state prediction is a multivariate problem, there is a need to maintain the spatiotemporal traffic state correlations. Two cutting-edge machine learning algorithms are used to predict the stretch-wide traffic stream traffic state up to 120 minutes in the future. Furthermore, the divide and conquer approach was used to divide the large prediction problem into a set of smaller overlapping problems. These smaller problems are solved using a medium configuration PC in a reasonable time (less than a minute), which makes the proposed technique suitable for practical applications.

References

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Paper Citation


in Harvard Style

Elhenawy M., Rakha H. and Chen H. (2016). Traffic Stream Short-term State Prediction using Machine Learning Techniques . In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-185-4, pages 124-129. DOI: 10.5220/0005895701240129


in Bibtex Style

@conference{vehits16,
author={Mohammed Elhenawy and Hesham Rakha and Hao Chen},
title={Traffic Stream Short-term State Prediction using Machine Learning Techniques},
booktitle={Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2016},
pages={124-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005895701240129},
isbn={978-989-758-185-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Traffic Stream Short-term State Prediction using Machine Learning Techniques
SN - 978-989-758-185-4
AU - Elhenawy M.
AU - Rakha H.
AU - Chen H.
PY - 2016
SP - 124
EP - 129
DO - 10.5220/0005895701240129