Methods for forecasting freight in uncertainty : time series analysis of multiple factors.
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Methods for forecasting freight in uncertainty : time series analysis of multiple factors.

Filetype[PDF-511.37 KB]


  • English

  • Details:

    • Corporate Contributors:
    • Publication/ Report Number:
    • Resource Type:
    • Geographical Coverage:
    • OCLC Number:
      724578081
    • Edition:
      Final report; Jan. 31, 2011.
    • Corporate Publisher:
    • NTL Classification:
      NTL-FREIGHT-FREIGHT ; NTL-FREIGHT-Freight Planning and Policy ; NTL-REFERENCES AND DIRECTORIES-Statistics ;
    • Abstract:
      The main goal of this research was to analyze and more accurately model freight movement in

      Alabama. Ultimately, the goal of this project was to provide an overall approach to the

      integration of accurate freight models into transportation plans and models in Alabama.

      The first step in the process was to identify the dependent variable and collect the data necessary

      to develop the models. Initially, Truck Vehicle Miles Traveled (VMT) was the preferred

      dependent variable however, data collection revealed that the available VMT was not

      particularly accurate since it is derived from VMT data for all vehicles and there was no

      validated method for estimating the percentage of trucks in any one year. Therefore, the research

      team determined that annual Diesel Tax collections would be a good surrogate for Truck VMT.

      The Diesel Tax collections were used to estimate the Diesel Gallons Sold each year by dividing

      by the tax rate for that year. This variable, Diesel Gallons Sold (DGS), has the advantage that it

      could be used to estimate annual truck volumes based on estimate mileage performance for

      trucks. Thus, DGS was chosen as the dependent variable for this study.

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