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Sep 8, 2022
ANN-Bayes-Based Travel Time Prediction Method for Signalized Corridors
Authors: Wei Lin [email protected], Heng Wei, Ph.D., F.ASCE [email protected], Dong Nian [email protected], and Mengmeng Zhang, Ph.D. [email protected]Author Affiliations
Publication: CICTP 2022
ABSTRACT
Uncertainties always lie in predicting travel time along a signalized corridor or dynamic urban network. Frequent interrupted traffic flows, varying signal timing schemes, crossing traffic, and dynamic route choices make arterial travel time estimations much more challenging than freeways. This paper presents a study of developing an Artificial Neural Network techniques and Bayes algorithms for signalized corridors travel time prediction. Two types of models are tested with the data source obtained along the US-27 corridor in Ohio, including hourly instantaneous travel time prediction model and realized route travel time prediction model. The study suggests that the proposed methods can effectively capture the travel time patterns by combining a base profile and an ANN-Bayes-trained dynamic profile, and make it more sensitive to a variation aroused by non-recurring congestion. The testing results indicate a good performance of the predicted travel time models that the most errors fall into the range of 0.608 to 0.485, much lower than the threshold standard deviation (2.24) and close to 6% of mean value (0.077).
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Published online: Sep 8, 2022
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1Ph.D. Candidate, Dept. of Civil and Architectural Engineering and Construction Management, Univ. of Cincinnati, Cincinnati, OH. Email: [email protected]
P.E.
2Guest Professor, Shandong Jiaotong Univ., China; Professor, Dept. of Civil and Architectural Engineering and Construction Management, Univ. of Cincinnati, Cincinnati, OH. ORCID: https://orcid.org/0000-0001-5308-8593. Email: [email protected]
3Ph.D. Candidate, Dept. of Civil and Architectural Engineering and Construction Management, Univ. of Cincinnati, Cincinnati, OH. Email: [email protected]
4Professor, School of Transportation and Logistics Engineering, Shandong Jiaotong Univ., Jinan, China. Email: [email protected]
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.