Staff View: A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression

A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression

Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improve...

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Main Authors:	Jawad-ur-Rehman Chughtai, Irfan ul Haq, Saif ul Islam, Abdullah Gani
Format:	Article
Language:	English
Published:	MDPI 2022
Subjects:	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware
Online Access:	https://eprints.ums.edu.my/id/eprint/42546/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42546/ https://doi.org/10.3390/s22249735
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spelling	my.ums.eprints.425462025-01-07T03:39:38Z https://eprints.ums.edu.my/id/eprint/42546/ A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression Jawad-ur-Rehman Chughtai Irfan ul Haq Saif ul Islam Abdullah Gani QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages–initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50 ), mean absolute error (12.22±1.35 ) and the coefficient of determination (0.99784±0.00019 ). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques. MDPI 2022 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42546/1/FULL%20TEXT.pdf Jawad-ur-Rehman Chughtai and Irfan ul Haq and Saif ul Islam and Abdullah Gani (2022) A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression. Sensors, 22. pp. 1-20. https://doi.org/10.3390/s22249735
institution	Universiti Malaysia Sabah
building	UMS Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sabah
content_source	UMS Institutional Repository
url_provider	http://eprints.ums.edu.my/
language	English
topic	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware
spellingShingle	QA75.5-76.95 Electronic computers. Computer science TK7885-7895 Computer engineering. Computer hardware Jawad-ur-Rehman Chughtai Irfan ul Haq Saif ul Islam Abdullah Gani A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
description	Travel time prediction is essential to intelligent transportation systems directly affecting smart cities and autonomous vehicles. Accurately predicting traffic based on heterogeneous factors is highly beneficial but remains a challenging problem. The literature shows significant performance improvements when traditional machine learning and deep learning models are combined using an ensemble learning approach. This research mainly contributes by proposing an ensemble learning model based on hybridized feature spaces obtained from a bidirectional long short-term memory module and a bidirectional gated recurrent unit, followed by support vector regression to produce the final travel time prediction. The proposed approach consists of three stages–initially, six state-of-the-art deep learning models are applied to traffic data obtained from sensors. Then the feature spaces and decision scores (outputs) of the model with the highest performance are fused to obtain hybridized deep feature spaces. Finally, a support vector regressor is applied to the hybridized feature spaces to get the final travel time prediction. The performance of our proposed heterogeneous ensemble using test data showed significant improvements compared to the baseline techniques in terms of the root mean square error (53.87±3.50 ), mean absolute error (12.22±1.35 ) and the coefficient of determination (0.99784±0.00019 ). The results demonstrated that the hybridized deep feature space concept could produce more stable and superior results than the other baseline techniques.
format	Article
author	Jawad-ur-Rehman Chughtai Irfan ul Haq Saif ul Islam Abdullah Gani
author_facet	Jawad-ur-Rehman Chughtai Irfan ul Haq Saif ul Islam Abdullah Gani
author_sort	Jawad-ur-Rehman Chughtai
title	A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
title_short	A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
title_full	A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
title_fullStr	A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
title_full_unstemmed	A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
title_sort	heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression
publisher	MDPI
publishDate	2022
url	https://eprints.ums.edu.my/id/eprint/42546/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42546/ https://doi.org/10.3390/s22249735
_version_	1821003229941465088
score	13.226497

A heterogeneous ensemble approach for travel time prediction using hybridized feature spaces and support vector regression

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