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Improving Existing Travel Models and Forecasting Processes: A White Paper


Original: October 17, 2013

Final: December 18, 2013

Prepared by:

2200 Wilson Blvd., Suite 205
Arlington, VA 22201

Prepared for:

Federal Highway Administration

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1. Report No.

2. Government Accession No.

3. Recipient's Catalog No.

4. Title and Subtitle
Improving Existing Travel Models and Forecasting Processes: A White Paper

5. Report Date
December 18, 2013


6. Performing Organization Code

7. Authors
William A. Woodford

8. Performing Organization Report No.

9. Performing Organization Name and Address
2200 Wilson Boulevard, Suite 205
Arlington, VA 22201

10. Work Unit No. (TRAIS)

  1. Contract or Grant No.

DTFH61-12-D-00013, T-12002

12. Sponsoring Agency Name and Address
United States Department of Transportation
Federal Highway Administration
1200 New Jersey Ave. SE
Washington, DC 20590

13. Type of Report and Period Covered
September 2013 - September 2015

14. Sponsoring Agency Code

15. Supplementary Notes
The project was managed by Sarah Sun, COR for Federal Highway Administration
The report has been reviewed by TMIP Travel Analysis Toolbox Project Independent Review Panel members

16. Abstract
Travel forecasts are critical inputs to transportation investment and policy decisions and help to introduce reason-based rigor into the planning process. Unfortunately, current practice in travel forecasting has several deficiencies that often diminish the value of these forecasts. These shortcomings are documented in TRB Special Report 288 and include: (a) Inherent weakness of the models, (b) errors introduced by modeling practice, (c) lack or questionable reliability of data, and (d) biases arising from the institutional climate in which models are used.

Forecasts and the models that underlie the forecasts can be strengthened by improving the practice of forecasting and by developing modeling tools that are thoroughly tested to confirm that they properly reflect transportation supply and demand. Some of the most important steps include:

  1. Improve the practice of travel forecasting;
  2. Collect better data;
  3. Confirm applicability of input assumptions;
  4. Improve the capabilities of existing tools;
  5. Confirming model validity.

Good forecasts require the judgment of the analyst who assembles the data and uses this information to understand how future travel characteristics will evolve. It also requires careful scrutiny by independent reviewers who examine both the analytical methods and the outcomes to determine the likelihood of the projected results.

17. Key Words
travel forecasting

18. Distribution Statement
No restrictions. This document is available to the public at

19. Security Classif. (of this report)

20. Security Classif. (of this page)

21. No. of Pages

22. Price

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Updated: 11/25/2014
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