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Post-Construction Evaluation of Traffic Forecast Accuracy


  • Pavithra Parthasarathi
  • David Levinson

    () (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)


This research evaluates the accuracy of demand forecasts using a sample of recently-completed projects in Minnesota and identifies the factors influencing the inaccuracy in forecasts. The forecast traffic data for this study is drawn from Environmental Impact Statements (EIS), Transportation Analysis Reports (TAR) and other forecast reports produced by the Minnesota Department of Transportation (Mn/DOT) with a horizon forecast year of 2010 or earlier. The actual traffic data is compiled from the database of traffic counts maintained by the Office of Traffic Forecasting and Analysis section at Mn/DOT. Based on recent research on forecast accuracy, the (in)accuracy of traffic forecasts is estimated as a ratio of the forecast traffic to the actual traffic. The estimation of forecast (in)accuracy also involves a comparison of the socioeconomic and demographic assumptions, the assumed networks to the actual in-place networks and other travel behavior assumptions that went into generating the traffic forecasts against actual conditions. The analysis indicates a general trend of underestimation in roadway traffic forecasts with factors such as highway type, functional classifications, direction playing an influencing role. Roadways with higher volumes and higher functional classifications such as freeways are subject to underestimation compared to lower volume roadways/functional classifications. The comparison of demographic forecasts shows a trend of overestimation while the comparison of travel behavior characteristics indicates a lack of incorporation of fundamental shifts and societal changes.

Suggested Citation

  • Pavithra Parthasarathi & David Levinson, 2008. "Post-Construction Evaluation of Traffic Forecast Accuracy," Working Papers 201005, University of Minnesota: Nexus Research Group.
  • Handle: RePEc:nex:wpaper:forecastaccuracy

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    References listed on IDEAS

    1. Flyvbjerg, Bent, 2005. "Measuring inaccuracy in travel demand forecasting: methodological considerations regarding ramp up and sampling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(6), pages 522-530, July.
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    4. Gerard Jong & Andrew Daly & Marits Pieters & Stephen Miller & Ronald Plasmeijer & Frank Hofman, 2007. "Uncertainty in traffic forecasts: literature review and new results for The Netherlands," Transportation, Springer, vol. 34(4), pages 375-395, July.
    5. Yong Zhao & Kara Maria Kockelman, 2002. "The propagation of uncertainty through travel demand models: An exploratory analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 36(1), pages 145-163.
    6. Kang-tsung Chang & Zaher Khatib & Yanmei Ou, 2002. "Effects of zoning structure and network detail on traffic demand modeling," Environment and Planning B: Planning and Design, Pion Ltd, London, vol. 29(1), pages 37-52, January.
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    Cited by:

    1. Vij, Akshay & Walker, Joan L., 2014. "Preference endogeneity in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 90-105.
    2. Eliasson, Jonas & Börjesson, Maria & van Amelsfort, Dirk & Brundell-Freij, Karin & Engelson, Leonid, 2013. "Accuracy of congestion pricing forecasts," Transportation Research Part A: Policy and Practice, Elsevier, vol. 52(C), pages 34-46.
    3. Andersson, Matts & Brundell-Freij, Karin & Eliasson, Jonas, 2017. "Validation of aggregate reference forecasts for passenger transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 101-118.
    4. Xu, Xiangdong & Chen, Anthony & Wong, S.C. & Cheng, Lin, 2015. "Selection bias in build-operate-transfer transportation project appraisals," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 245-251.
    5. Sanko, Nobuhiro & Morikawa, Takayuki & Nagamatsu, Yoshitaka, 2013. "Post-project evaluation of travel demand forecasts: Implications from the case of a Japanese railway," Transport Policy, Elsevier, vol. 27(C), pages 209-218.
    6. Maria Börjesson & Jonas Eliasson & Mattias Lundberg, 2014. "Is CBA Ranking of Transport Investments Robust?," Journal of Transport Economics and Policy, University of Bath, vol. 48(2), pages 189-204, May.
    7. Nicolaisen, Morten Skou & Næss, Petter, 2015. "Roads to nowhere: The accuracy of travel demand forecasts for do-nothing alternatives," Transport Policy, Elsevier, vol. 37(C), pages 57-63.
    8. Pavithra Parthasarathi & David Levinson & Hartwig Hochmair, 2012. "Network Structure and Travel Time Perception," Working Papers 000102, University of Minnesota: Nexus Research Group.
    9. Salling, Kim Bang & Leleur, Steen, 2015. "Accounting for the inaccuracies in demand forecasts and construction cost estimations in transport project evaluation," Transport Policy, Elsevier, vol. 38(C), pages 8-18.
    10. Morten Skou Nicolaisen & Patrick A. Driscoll, 2016. "An International Review of Ex-Post Project Evaluation Schemes in the Transport Sector," Journal of Environmental Assessment Policy and Management (JEAPM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-33, March.
    11. Manzo, Stefano & Nielsen, Otto Anker & Prato, Carlo Giacomo, 2015. "How uncertainty in input and parameters influences transport model :output A four-stage model case-study," Transport Policy, Elsevier, vol. 38(C), pages 64-72.
    12. Odeck, James, 2013. "How accurate are national road traffic growth-rate forecasts?—The case of Norway," Transport Policy, Elsevier, vol. 27(C), pages 102-111.
    13. Andersson, Matts & Brundell-Freij, Karin & Eliasson, Jonas, 2016. "Validation of reference forecasts for passenger transport," Working papers in Transport Economics 2016:15, CTS - Centre for Transport Studies Stockholm (KTH and VTI), revised 07 Jul 2016.

    More about this item


    Minnesota; Minneapolis; Travel Demand Model; Transportation Planning; Forecasting;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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