An adaptive model for highway travel time prediction |
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Authors: | Xiaobo Liu Steven I Chien Mei Chen |
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Institution: | 1. Department of Planning, Jacobs Engineering Group, Morristown, NJ, U.S.A.;2. College of Automobile, Chang'an University, Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ, U.S.A.;3. Department of Civil Engineering, University of Kentucky, Lexington, KY, U.S.A. |
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Abstract: | Traffic congestion caused by either insufficient road capacity or unexpected events has been a major problem in urban transportation networks. To disseminate accurate traveler information and reduce congestion impact, it is desirable to develop an adaptive model to predict travel time. The proposed model is practically implementable to capture dynamic traffic patterns under various conditions, which integrates the features of exponential smoothing and the Kalman filter by utilizing both real‐time and historic data. The model is simple in formulation while robust in performance in terms of accuracy and stability. With a constraint or nonconstraint smoothing factor, the proposed model is tested with both real world and simulated data and demonstrated itself a sound model that outperforms others (e.g., Kalman filter and simple exponential smoothing) specifically under recurring and nonrecurring congestion. Copyright © 2012 John Wiley & Sons, Ltd. |
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Keywords: | travel time prediction simple exponential smoothing Kalman filter ITS |
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