Improved DTTE Method for Route-Level Travel Time Estimation on Freeways
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 2
Abstract
Travel time estimation plays an important role in advanced traveler information systems (ATIS) for dynamic traffic management. Static travel time estimation (STTE) and dynamic travel time estimation (DTTE) are two of the major methods widely explored for travel time measurement. To analyze their performance on route-level travel time estimation on freeways where congestion may occur, this study developed a framework consisting of four steps: traffic state prediction, travel time estimation, results evaluation, and performance comparison. A METANET-based macroscopic traffic model was developed and employed to predict traffic states based on loop detector data. Then, a novel DTTE method was developed and is proposed herein that combines the piece-wise linear speed-based (PLSB) method and the trajectory assumption algorithm. The indices of the mean absolute relative error (MARE) and the root mean squared error (RMSE) were employed to analyze estimation accuracy by the traditional STTE method and the proposed DTTE method. The comparison results illustrate that during high-demand periods, the proposed DTTE method outperforms the traditional STTE method by producing results that better match reference travel times, which were obtained from video sensors installed along the urban freeway corridor.
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Data Availability Statement
Some or all data used in this study were provided by a third party. Direct request for these materials may be made to the provider as indicated in the Acknowledgments.
Acknowledgments
The authors would like to thank the Centre for Smart Transportation at the University of Alberta and the City of Edmonton for providing data used in this paper. This research work was jointly supported by the Innovation Program of Shanghai Municipal Education Commission (2021-01-07-00-07-E00092), Shanghai Engineering Research Center of Urban Infrastructure Renewal (No. 20DZ2251900), Key Research and Development Program of Shandong Province (2020CXGC010117), and Shandong Transportation Technology Plan (2021B60).
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Received: Jun 24, 2021
Accepted: Oct 21, 2021
Published online: Dec 7, 2021
Published in print: Feb 1, 2022
Discussion open until: May 7, 2022
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