A retrospective evaluation of traffic forecasting techniques.

Details

• Creators:
  Miller, John S. ; Anam, Salwa ; Amanin, Jasmine W. ; Matteo, Raleigh A.
• Corporate Creators:
  Virginia Transportation Research Council
• Corporate Contributors:
  Virginia. Dept. of Transportation ; United States. Federal Highway Administration. Virginia Division
• Subject/TRT Terms:
  Accuracy Mathematical Prediction Traffic Forecasting Traffic Volume Travel Demand Uncertainty
• Publication/ Report Number:
  FHWA/VTRC 17-R1
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Virginia
• OCLC Number:
  958270389
• Edition:
  Final report
• Corporate Publisher:
  Virginia Transportation Research Council
• Abstract:
  Traffic forecasting techniques—such as extrapolation of previous years’ traffic volumes, regional travel demand models, or
  
  local trip generation rates—help planners determine needed transportation improvements. Thus, knowing the accuracy of these
  
  techniques can help analysts better consider the range of transportation investments for a given location. To determine this
  
  accuracy, the forecasts from 39 Virginia studies (published from 1967-2010) were compared to observed volumes for the forecast
  
  year. Excluding statewide forecasts, the number of segments in each study ranged from 1 to 240. For each segment, the
  
  difference between the forecast volume and the observed volume divided by the observed volume gives a percent error such that a
  
  segment with a perfect forecast has an error of 0%. For the 39 studies, the median absolute percent error ranged from 1% to
  
  134%, with an average value of 40%. Slightly more than one-fourth of the error was explained by three factors: the method used
  
  to develop the forecast, the length of the duration between the base year and forecast year, and the number of economic recessions
  
  between the base year and forecast year. In addition, although data are more limited, studies that forecast a 24-hour volume had a
  
  smaller percent error than studies that forecast a peak hour volume (p = 0.04); the reason is that the latter type of forecast requires
  
  an additional data element—the peak hour factor—that itself must be forecast. A limitation of this research is that although
  
  replication of observed volumes is sought when making a forecast, the observed volumes themselves are not without error; for
  
  example, an “observed” traffic count for a given year may in fact be based on a 48-hour count that has been expanded, based on
  
  seasonal adjustment factors, to estimate a yearly average traffic volume.
  
  The primary recommendation of this study is that forecasts be presented as a range. For example, based on the 39 studies
  
  evaluated, for a study that provides forecasts for multiple links, one would expect the median percent error to be approximately
  
  40%. To be clear, detailed analysis of one study suggests it is possible that even a forecast error will not necessarily alter the
  
  decision one would make based on the forecast. Accordingly, considering how a change in a traffic forecast volume (by the
  
  expected error) influences decisions can help one better understand the need for a given transportation improvement. A
  
  secondary recommendation is to clarify how some of these traffic forecasting techniques can be performed, and supporting details
  
  for this clarification are given in Appendix A of this report.
  
  
• Format:
  PDF
• Alternate URL:
  http://www.virginiadot.org/vtrc/main/online_reports/pdf/17-r1.pdf
• Funding:
  106068
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:c51f251c9f07bc4e0bd10dd3ccbe516c5e7dccb9f35ef5f1df0f39f528a6b857
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31194/dot_31194_DS1.pdf
• File Type:
  [PDF - 1.18 MB ]