loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock
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

ISBN: 978-989-758-185-4

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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.171.45.91

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - Volume 1: VEHITS, ISBN 978-989-758-185-4, 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 - Volume 1: VEHITS,},
year={2016},
pages={124-129},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005895701240129},
isbn={978-989-758-185-4},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.