Travel Time Modeling using Spatiotemporal Speed Variation and a Mixture of Linear Regressions

Mohammed Elhenawy, Abdallah A. Hassan, Hesham A. Rakha

2018

Abstract

Real-time, accurate travel time prediction algorithms are needed for individual travelers, business sectors, and government agencies. They help commuters make better travel decisions, avert traffic congestion, help the environment by reducing carbon emissions, and improve traffic efficiency. Travel time prediction has begun to attract more attention with the rapid development of intelligent transportation systems (ITSs), and is considered one of the more important elements required for successful ITS subsystems deployment. However, the stochastic nature of travel time makes accurate prediction a difficult task. This paper proposes travel time modeling using a mixture of linear regressions. The proposed model consists of two normal components. The first component models the congested regime while the other models the free-flow regime. The means of the two components are modeled by two linear regression equations. The predictors used in the linear regression equation are selected out of the spatiotemporal speed matrix using a random forest machine-learning algorithm. The proposed model is tested using archived data from a 74.4-mile freeway stretch of I-66 eastbound connecting I-81 and Washington, D.C. The experimental results show the ability of the model to capture the stochastic nature of travel time and to predict travel time accurately.

Paper Citation

in Harvard Style

Elhenawy M., Hassan A. and A. Rakha H. (2018). Travel Time Modeling using Spatiotemporal Speed Variation and a Mixture of Linear Regressions.In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-293-6, pages 113-120. DOI: 10.5220/0006690601130120

in Bibtex Style

@conference{vehits18,
author={Mohammed Elhenawy and Abdallah A. Hassan and Hesham A. Rakha},
title={Travel Time Modeling using Spatiotemporal Speed Variation and a Mixture of Linear Regressions},
booktitle={Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2018},
pages={113-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006690601130120},
isbn={978-989-758-293-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Travel Time Modeling using Spatiotemporal Speed Variation and a Mixture of Linear Regressions
SN - 978-989-758-293-6
AU - Elhenawy M.
AU - Hassan A.
AU - A. Rakha H.
PY - 2018
SP - 113
EP - 120
DO - 10.5220/0006690601130120