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Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage

1
Department of Civil Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait
2
Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(14), 3822; https://doi.org/10.3390/su11143822
Received: 12 June 2019 / Revised: 2 July 2019 / Accepted: 8 July 2019 / Published: 12 July 2019
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Abstract

The objective of this study is to estimate the real time travel times on urban networks that are partially covered by moving sensors. The study proposes two machine learning approaches; the random forest (RF) model and the multi-layer feed forward neural network (MFFN) to estimate travel times on urban networks which are partially covered by moving sensors. A MFFN network with three hidden layers was developed and trained using the back-propagation learning algorithm, and the neural weights were optimized using the Levenberg–Marquardt optimization technique. A case study of an urban network with 100 links is considered in this study. The performance of the proposed models was compared to a statistical model, which uses the empirical Bayes (EB) method and the spatial correlation between travel times. The models’ performances were evaluated using data generated from VISSIM microsimulation model. Results show that the machine learning algorithms, e.g., RF and ANN, achieve average improvements of about 4.1% and 2.9% compared with the statistical approach. The RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracies reaching 90.7%, 89.5%, and 86.6% respectively. Moreover, results show that at low moving sensor penetration rate, the RF and MFFN achieve higher estimation accuracy compared with the statistical approach. At probe penetration rate of 1%, the RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracy of 85.6%, 84.4%, and 80.9% respectively. Furthermore, the study investigated the impact of the probe penetration rate on real time neighbor links coverage. Results show that at probe penetration rates of 1%, 3%, and 5%, the models cover the estimation of real time travel times on 73.8%, 94.8%, and 97.2% of the estimation intervals. View Full-Text
Keywords: machine learning; random forest; neural network; ITS; travel time estimation machine learning; random forest; neural network; ITS; travel time estimation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Alrukaibi, F.; Alsaleh, R.; Sayed, T. Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage. Sustainability 2019, 11, 3822.

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