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

Details

• Creators:
  Farrington, Phillip A.
• Corporate Creators:
  University of Alabama in Huntsville
• Corporate Contributors:
  Alabama. Department of Transportation
• Subject/TRT Terms:
  • Time series analysis
  • Forecasting
  • Freight and passenger traffic
  • Freight and freightage--Forecasting--Mathematical models
  • Time-series analysis
• Publication/ Report Number:
  930-768
• Resource Type:
  Tech Report
• Geographical Coverage:
  Alabama
• Edition:
  Final report; Jan. 31, 2011.
• Corporate Publisher:
  University of Alabama
• 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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• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/20365/dot_20365_DS1.pdf
• File Type:
  [PDF - 511.37 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:1175b300e0e76f4b62fe92a92703b3142d86406184c338f4a60a0f4bc47dac7d51b37df69190309110c55ce54eab869775147cba29e0a3970056267e053bf5db