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Traffic Stream Short-term State Prediction using Machine Learning Techniques

Topics: Big Data Analytics for Intelligent Transportation; Big Data and Vehicle Analytics; Congestion Management and Avoidance; Cooperative Driving and Traffic Management; Intelligent Infrastructure and Guidance Systems; Traffic and Vehicle Data Collection and Processing

Authors: Mohammed Elhenawy ; Hesham Rakha and Hao Chen

Affiliation: Virginia Tech Transportation Institute, United States

Keyword(s): Transportation Planning and Traffic Operation, Real-time Automatic Congestion Identification, Mixture of Linear Regression, ITS.

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.

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Paper citation in several formats:
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 - VEHITS; ISBN 978-989-758-185-4; ISSN 2184-495X, SciTePress, pages 124-129. DOI: 10.5220/0005895701240129

@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 - VEHITS},
year={2016},
pages={124-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005895701240129},
isbn={978-989-758-185-4},
issn={2184-495X},
}

TY - CONF

JO - Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - VEHITS
TI - Traffic Stream Short-term State Prediction using Machine Learning Techniques
SN - 978-989-758-185-4
IS - 2184-495X
AU - Elhenawy, M.
AU - Rakha, H.
AU - Chen, H.
PY - 2016
SP - 124
EP - 129
DO - 10.5220/0005895701240129
PB - SciTePress

- Science and Technology Publications, Lda.
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