Advertisement

Transportation

, Volume 41, Issue 4, pp 819–837 | Cite as

Tollroads are only part of the overall trip: the error of our ways in past willingness to pay studies

  • John M. Rose
  • David A. Hensher
Article

Abstract

With rare exception, actual tollroad traffic in many countries has failed to reproduce forecast traffic levels, regardless of whether the assessment is made after an initial year of operation or as long as 10 years after opening. Pundits have offered many reasons for this divergence, including optimism bias, strategic misrepresentation, the promise to equity investors of early returns on investment, errors in land use forecasts, and specific assumptions underlying the traffic assignment models used to develop traffic forecasts. One such assumption is the selection of a behaviourally meaningful value of travel time savings (VTTS) for use in a generalised cost or generalised time user benefit expression that is the main behavioural feature of the traffic assignment (route choice) model. Numerous empirical studies using stated choice experiments have designed choice sets of alternatives as if users choose a tolled route or a free route under the (implied) assumption that the tolled route is tolled for the entire trip. Reality is often very different, with a high incidence of use of a non-tolled road leading into and connecting out of a tolled link. In this paper we recognise this feature of route choice and redesign the stated choice experiment to account for it. Furthermore, this study is a follow up to a previous study undertaken before a new toll road was in place, and it benefits from real exposure to the new toll road. We find that the VTTS is noticeably reduced, and if the VTTS is a significant contributing influence on errors on traffic forecasts, then the lower estimates make sense behaviourally.

Keywords

Value of travel time savings Toll routes Free routes Choice experiment Errors in forecasts 

Notes

Acknowledgments

We thank the three referees for their comments as well as Stephane Hess who have provided invaluable comments which have resulted in numerous improvements to the paper.

References

  1. Auger, P., Devinney, T.M., Louviere, J.J.: Best-Worst scaling methodology to investigate consumer ethical beliefs across countries. J. Bus. Ethics 70, 299–326 (2007)CrossRefGoogle Scholar
  2. Bain, R.: Error and optimism bias in toll road traffic forecasts. Transportation 36(5), 469–482 (2009a)CrossRefGoogle Scholar
  3. Bain, R.: Toll Road Traffic and Revenue Forecasts. An Interpreters Guide. (2009b) ISBN 978-0-9561527-1-8Google Scholar
  4. Bain, R.: On the reasonableness of traffic forecasts, pp. 213–217. TEC Magazine, May (2011)Google Scholar
  5. Brownstone, D., Ghosh, A., Golob, T.F., Kazimi, C., Van Amelsfort, D.: Drivers’ willingness-to-pay to reduce travel time: evidence from the San Diego I-15 congestion pricing project. Transp. Res. Part A 37(4), 373–387 (2003)Google Scholar
  6. Chapman, R.G., Staelin, R.: Exploiting rank ordered choice set data within the stochastic utility model. J. Mark. Res. 19, 288–301 (1982)CrossRefGoogle Scholar
  7. Cohen, E.: Applying best-worst scaling to wine marketing. Int. J. Wine Bus. Res. 21(1), 8–23 (2009)CrossRefGoogle Scholar
  8. Flyvbjerg, B., Bruzelius, N., Rothengatter, W.: Megaprojects and risk: an anatomy of ambition. Cambridge University Press, Cambridge (2003)Google Scholar
  9. Flyvbjerg, B., Skamris Holm, M., Buhl, S.L.: Inaccuracy in traffic forecasts. Transp. Rev. 26(1), 1–24 (2006)CrossRefGoogle Scholar
  10. Flynn, T.N., Louviere, J.J., Peters, T.J., Coast, J.: Best-Worst scaling: what it can do for health care research and how to do it. J. Health Econ. 26, 171–189 (2007)CrossRefGoogle Scholar
  11. Hatton MacDonald, D., Morrison, M., Rose, J.M., Boyle, K.: Untangling differences in values from internet and mail stated preference studies, Fourth World Congress of Environmental and Resource Economists, Montreal Canada, June 28–July 2 (2010)Google Scholar
  12. Hausman, J.A., Ruud, P.A.: Specifying and testing econometric models for rank-ordered data. J. Econ. 34, 83–103 (1987)CrossRefGoogle Scholar
  13. Hess, S., Rose, J.M., Hensher, D.A.: Asymmetrical preference formation in willingness to pay estimates in discrete choice models. Transp. Res. Part E 44(5), 847–863 (2008)CrossRefGoogle Scholar
  14. Krinsky, I., Robb, A.L.: On approximating the statistical properties of elasticities. Rev. Econ. Stat. 68(4), 715–719 (1986)CrossRefGoogle Scholar
  15. Krinsky, I., Robb, A.L.: On approximating the statistical properties of elasticities: a correction. Rev. Econ. Stat. 72(1), 189–190 (1990)CrossRefGoogle Scholar
  16. Li, Z., Hensher, D.A.: Toll roads in Australia. Transp. Rev. 30(5), 541–569 (2010)CrossRefGoogle Scholar
  17. Lindhjem, H., Navrud, S.: using internet in stated preference surveys: a review and comparison of survey modes. Int. Rev. Environ. Resour. Econ. 5, 309–351 (2011)CrossRefGoogle Scholar
  18. Louviere, J.J., Islam, T.: A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling. J. Bus. Res. 61, 903–911 (2008)CrossRefGoogle Scholar
  19. Marley, A., Louviere, J.J.: Some probabilistic models of best, worst, and best-worst choices. J. Math. Psychol. 49, 464–480 (2005)CrossRefGoogle Scholar
  20. Marley, A.A.J., Pihlens, D.: Models of best-worst choice and ranking among multi-attribute options (profiles). J. Math. Psychol. 56, 24–34 (2012)CrossRefGoogle Scholar
  21. Orne, M.T.: Demand characteristics and the concept of quasi-controls. In: Rosenthal, R., Rosnow, R.L., Kazdin, A.E. (eds.) Artifact in behavioral research, pp. 143–179. Academic Press, New York (1969)Google Scholar
  22. Orne, M.T.: The demand characteristics of an experimental design and their implications. Paper presented at the American Psychological Association, Cincinnati (1959)Google Scholar
  23. Rose, J.M., Bliemer, M.C., Hensher, D.A., Collins, A.T.: Designing efficient stated choice experiments in the presence of reference alternatives. Transp. Res. Part B 42(4), 395–406 (2008)CrossRefGoogle Scholar
  24. Rose, J.M., Bliemer, M.C.J.: Constructing efficient stated choice experimental designs. Transp. Rev. 29(5), 587–617 (2009)CrossRefGoogle Scholar
  25. Smith, K., Shahidullah, M.: An evaluation of population projection errors for census tracts, Journal of the American Statistical Association, March 90 (429), Applications and Case Studies. Train, K. (2009) Discrete Choice Methods with Simulation 2nd Ed. Cambridge University Press, Cambridge (1995)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Institute of Transport and Logistics Studies (ITLS), The University of Sydney Business School, The University of SydneyDarlingtonAustralia

Personalised recommendations