Abstract Time headway distribution modeling is fundamental to many aspects of traffic flow studies such as capacity estimation, safety analysis, and microscopic simulation. Existing time headway distribution models have focused on the behavior of general vehicles. We examine the distribution of sampled vehicle headway (e.g., probes) on both single and multiple lane highway traffic streams. This study is divided into three parts: an empirical study, a simulation analysis, and an analytical derivation. The empirical study uses probe data obtained from Houston, Texas that was collected as part of the Automatic Vehicle Identification system of the Houston Transtar system. In the empirical study, a shifted negative exponential distribution was found to give the closest fit for both single and multiple lane cases. We found that if the volume level of the probes is low, regardless of the volume level of general vehicles, the headway of the probes followed a shifted negative exponential distribution. In the simulation study, we found that the time headway of probes does not necessarily follow the time headway distribution of general vehicles. Rather, it depends on many variables such as the volume level of general vehicles, the market penetration of probe vehicles, and the number of lanes. However, when the volume level of general vehicles is low, the headway of probes tends to follow the shifted negative exponential distribution at all levels of market penetration, together with the general vehicles. We analytically proved that if the time headway of general vehicles follows the shifted negative exponential distribution, then the time headway of the probes is the same as that of the general vehicles.


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    Titel :

    Time headway distribution of probe vehicles on single and multiple lane highways


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2013-05-01


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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