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Title	A Heterogeneous Ensemble Approach For Travel Time Prediction Using Hybridized Feature Spaces And Support Vector Regression
ID_Doc	2026
Authors	Chughtai J.-U.-R.; Haq I.U.; Islam S.U.; Gani A.
Year	2022
Published	Sensors, 22, 24
DOI	http://dx.doi.org/10.3390/s22249735
Abstract	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. © 2022 by the authors.
Author Keywords	heterogeneous ensemble learning; hybridized feature space; intelligent transportation systems; travel time prediction

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