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:
  • Traffic forecasting
  • Accuracy
  • Traffic volume
  • Travel demand
  • Uncertainty
  • Mathematical prediction
• 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.
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• Format:
  PDF
• Alternate URL:
  http://www.virginiadot.org/vtrc/main/online_reports/pdf/17-r1.pdf
• Funding:
  106068
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31194/dot_31194_DS1.pdf
• File Type:
  [PDF - 1.18 MB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:c51f251c9f07bc4e0bd10dd3ccbe516c5e7dccb9f35ef5f1df0f39f528a6b857