A 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.


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    Titel :

    Macroscopic traffic characterization based on driver memory and traffic stimuli


    Beteiligte:
    Zawar H. Khan (Autor:in) / Waheed Imran (Autor:in) / T. Aaron Gulliver (Autor:in) / Khurram S. Khattak (Autor:in) / Ghayas Ud Din (Autor:in) / Nasru Minallah (Autor:in) / Mushtaq A. Khan (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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