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Understanding the role of the forecast-maker in overestimation forecasts of policy impacts: The case of Travel Demand Management policies

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  • Tal, Gil
  • Cohen-Blankshtain, Galit
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    Abstract

    Forecasting the impacts of a proposed policy is an important component of the transportation planning and decision making process. Although scientific tools are often used in transportation forecasts, biases and, more specifically, overestimations of the expected impact are often observed. This study explores the correlations between forecast-maker's characteristics and forecast bias creation and reduction. The study examines two transport-related policies aiming at the reduction of car use: telecommuting and carsharing. Both are Travel Demand Management (TDM) policies, which attract much attention from transport experts. We tested the extent to which the forecast-maker's beliefs about the policy at stake affected the forecast bias. We found that attitudes and beliefs associates not only with overestimation bias but also with its reduction over time. We also tested the extent to which the forecast-maker's affiliation, the performing institute and the publication type were correlated with the biases of the forecast and with the forecaster attitudes and beliefs. These characteristics are intuitively used by the forecast user as tools to assess the 'objectivity' of the forecast, but our analysis found no association between these characteristics and the forecast bias.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

    Volume (Year): 45 (2011)
    Issue (Month): 5 (June)
    Pages: 389-400

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    Handle: RePEc:eee:transa:v:45:y:2011:i:5:p:389-400

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    Related research

    Keywords: Optimism bias Telecommuting Carsharing Forecasting Bias;

    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Brinkman, Anthony P., 2003. "The Ethical Challenges and Professional Responses of Travel Demand Forecasters," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt9c3330tt, Institute of Transportation Studies, UC Berkeley.
    2. Amos Tversky & Daniel Kahneman, 1979. "Prospect Theory: An Analysis of Decision under Risk," Levine's Working Paper Archive 7656, David K. Levine.
    3. Sangho Choo & Patricia L. Mokhtarian & Ilan Salomon, 2005. "Does Telecommuting Reduce Vehicle-miles Traveled? An Aggregate Time Series Analysis for the U. S," Econometrics 0505001, EconWPA.
    4. Brinkman, P. Anthony, 2003. "The Ethical Challenges and Professional Responses of Travel Demand Forecasters," University of California Transportation Center, Working Papers qt7vb2d17h, University of California Transportation Center.
    5. Shaheen, Susan & Sperling, Daniel & Wagner, Conrad, 1998. "Carsharing in Europe and North American: Past, Present, and Future," University of California Transportation Center, Working Papers qt4gx4m05b, University of California Transportation Center.
    6. Tal, Gil, 2008. "Overestimation Reduction in Forecasting Telecommuting as a TDM Policy," Institute of Transportation Studies, Working Paper Series qt1qk959v1, Institute of Transportation Studies, UC Davis.
    7. Mackie, Peter & Preston, John, 1998. "Twenty-one sources of error and bias in transport project appraisal," Transport Policy, Elsevier, vol. 5(1), pages 1-7, January.
    8. Marshall, Stephen & Banister, David, 2000. "Travel reduction strategies: intentions and outcomes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(5), pages 321-338, June.
    9. Richard Boylan, 2008. "Political distortions in state forecasts," Public Choice, Springer, vol. 136(3), pages 411-427, September.
    10. Meyer, Michael D., 1999. "Demand management as an element of transportation policy: using carrots and sticks to influence travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(7-8), pages 575-599.
    11. Mackett, Roger L. & Edwards, Marion, 1998. "The impact of new urban public transport systems: will the expectations be met?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(4), pages 231-245, May.
    12. Martin, Elliot & Shaheen, Susan Alison & Lidicker, Jeffrey, 2010. "Carsharing’S Impact On Household Vehicle Holdings: Results From A North American Shared-Use Vehicle Survey," Institute of Transportation Studies, Working Paper Series qt0850h6r5, Institute of Transportation Studies, UC Davis.
    13. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias In Macroeconomic Forecasts," The Quarterly Journal of Economics, MIT Press, vol. 114(1), pages 293-318, February.
    14. Steininger, Karl & Vogl, Caroline & Zettl, Ralph, 1996. "Car-sharing organizations : The size of the market segment and revealed change in mobility behavior," Transport Policy, Elsevier, vol. 3(4), pages 177-185, October.
    15. Baghestani, Hamid, 2008. "Federal Reserve versus private information: Who is the best unemployment rate predictor," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 101-110.
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    Cited by:
    1. Truschkin, Eugen & Elbert, Ralf, 2013. "Horizontal transshipment technologies as enablers of combined transport: Impact of transport policies on the modal split," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 91-109.
    2. Bastani, Parisa & Heywood, John B. & Hope, Chris, 2012. "The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 517-548.

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