Macroscopic traffic characterization based on driver memory and traffic stimuli

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dc.contributor.authorKhan, Zawar H.
dc.contributor.authorImran, Waheed
dc.contributor.authorGulliver, Thomas Aaron
dc.contributor.authorKhattak, Khurram S.
dc.contributor.authorDin, Ghayas Ud
dc.contributor.authorMinallah, Nasru
dc.contributor.authorKhan, Mushtaq A.
dc.date.accessioned2024-02-09T23:21:12Z
dc.date.available2024-02-09T23:21:12Z
dc.date.copyright2023en_US
dc.date.issued2023
dc.description.abstractA new macroscopic traffic flow model is proposed which incorporates traffic alignment behavior at transitions. In this model, velocity is a function of the distance headway and driver response time. It can be used to characterize the traffic flow for both uniform and non uniform headways. The well-known Zhang model characterizes this flow based on driver memory which can produce unrealistic results. The performance of the proposed Khan-Imran-Gulliver (KIG) and Zhang models is evaluated for an inactive bottleneck on a 2000 m circular road. The results obtained show that the traffic behavior with the KIG model is more realistic.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis Project was supported by the Higher Education Commission of Pakistan under the establishment of the National Center in Big Data and Cloud Computing at the University of Engineering and Technology, Peshawar.en_US
dc.identifier.citationKhan, Z. H., Imran, W., Gulliver, T. A., Khattak, K. S., Din, G. U., Minallah, N., & Khan, M. A. (2023). Macroscopic traffic characterization based on driver memory and traffic stimuli. Transportation Engineering, 14, 100208. https://doi.org/10.1016/j.treng.2023.100208en_US
dc.identifier.urihttps://doi.org/10.1016/j.treng.2023.100208
dc.identifier.urihttp://hdl.handle.net/1828/15989
dc.language.isoenen_US
dc.publisherTransportation Engineeringen_US
dc.subjectZhang model
dc.subjectmacroscopic traffic flow
dc.subjectdriver reaction
dc.subjectdistance headway
dc.subjectdriver memory
dc.subjectflow stability
dc.subjectnumerical stability
dc.subject.departmentDepartment of Electrical and Computer Engineering
dc.titleMacroscopic traffic characterization based on driver memory and traffic stimulien_US
dc.typeArticleen_US

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