Trucks play a significant role in transporting freight through the ground transportation systems, and it is important to be able to estimate and predict the amount of freight in a highway network. In this study, truck weight and volume data, obtained from Weigh-in-Motion stations in California, are analyzed to examine temporal and seasonal patterns. Then coupled with socio- economic variables, a hierarchical regression model is established for estimating freight demand. The model reveals that truck freight demand can be estimated by the truck annual average daily traffic along with the population, number of firms and median income at that county level. What is more, it has a predicting capability of freight weight where no truck weight data is measured. Therefore it can be served for practitioners to predict the growth of freight demand in a freeway network, and is useful to achieve the optimal flows design of cargo at all segments, regardless of whether or not weight data is available.


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    Titel :

    Hierarchical Regression Model for Analyzing Truck Freight Demand



    Erschienen in:

    Applied Mechanics and Materials ; 178-181 ; 2752-2756


    Erscheinungsdatum :

    2012-05-14


    Format / Umfang :

    5 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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