Traffic Estimation of Various Connected Vehicle Penetration Rates: Temporal Convolutional Network Approach

dc.contributor.authorAshqer, Mujahid
dc.contributor.authorAshqar, Huthaifa
dc.contributor.authorElhenawy, Mohammed
dc.contributor.authorRakha, Hesham A.
dc.contributor.authorBikdash, Marwan
dc.date.accessioned2024-10-28T14:31:16Z
dc.date.available2024-10-28T14:31:16Z
dc.date.issued2024-05
dc.description.abstractTraffic estimation using probe vehicle data is a crucial aspect of traffic management as it provides real-time information about traffic conditions. This study introduced a novel framework for traffic density estimation using Temporal Convolutional Network (TCN) for time series data. The study used two datasets collected from a three-leg intersection in Greece and a four-leg intersection in Germany. The model was built to predict the density in an approach of the signalized intersection using features extracted from the other approaches. The results showed that the highest accuracy was achieved when only probe vehicle data was used. This implies that relying solely on probe vehicle data from two approaches can effectively predict traffic density in the third approach, even when the Market Penetration Rate (MPR) is low. The results also indicated that having Signal Phase and Timing (SPaT) information may not be necessary for high accuracy in traffic estimation and that as the MPR increases, the model becomes more predictable.
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/10287204/
dc.format.extent10 pages
dc.genreconference papers and proceedings
dc.genrepreprints
dc.identifierdoi:10.13016/m2l9xp-mekq
dc.identifier.citationAshqer, Mujahid I., Huthaifa I. Ashqar, Mohammed Elhenawy, Hesham A. Rakha, and Marwan Bikdash. “Traffic Estimation of Various Connected Vehicle Penetration Rates: Temporal Convolutional Network Approach.” IEEE Transactions on Intelligent Transportation Systems 25, no. 5 (May 2024): 4326–34. https://doi.org/10.1109/TITS.2023.3322982.
dc.identifier.urihttps://doi.org/10.1109/TITS.2023.3322982
dc.identifier.urihttp://hdl.handle.net/11603/36814
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Data Science
dc.rights© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectDeep learning
dc.subjectEstimation
dc.subjectFeature extraction
dc.subjectcongestion
dc.subjectGlobal Positioning System
dc.subjectprobe vehicles
dc.subjectProbes
dc.subjectReal-time systems
dc.subjectRoads
dc.subjectSensors
dc.subjecttemporal convolutional network
dc.subjecttraffic density
dc.titleTraffic Estimation of Various Connected Vehicle Penetration Rates: Temporal Convolutional Network Approach
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-6835-8338

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