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Travel using managed lanes: An application of a stated choice model for Houston, Texas

  • Patil, Sunil
  • Burris, Mark
  • Douglass Shaw, W.

Managed lane (ML) travel adds flexibility, but also complexity, to travel choices. Stated choice models (SCMs) are often used for modeling complex transportation choices such as these in an effort to predict demand for these travel options. The design methods for SCMs have evolved from simple orthogonal designs to more sophisticated designs such as D-efficient design that can increase efficiency in estimation. We used three different survey design strategies to produce the stated preference portion of surveys, which were used to elicit travel choices for a sample of Houston travelers. Apart from the D-efficient design we also used random and adaptive random designs to generate attribute levels. There were observable differences in choice behavior depending on what design strategy was used. These differences appear to influence estimates of the value of travel time savings (VTTS) obtained from the random parameter logit (RPL) models estimated using these data. This, in turn, would greatly impact the percentage of travelers predicted to use the MLs. The adaptive random strategy was superior to the other design methods in several categories, and it had similar efficiency to the D-efficient design. However, the mean of VTTS estimate obtained from a D-efficient design was closer to what is typically found in the literature. The difference was considerable and could greatly influence traffic and revenue estimates for the MLs, illustrating the importance of the survey design strategy.

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Article provided by Elsevier in its journal Transport Policy.

Volume (Year): 18 (2011)
Issue (Month): 4 (August)
Pages: 595-603

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Handle: RePEc:eee:trapol:v:18:y:2011:i:4:p:595-603
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  1. James Murphy & P. Allen & Thomas Stevens & Darryl Weatherhead, 2005. "A Meta-analysis of Hypothetical Bias in Stated Preference Valuation," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 30(3), pages 313-325, 03.
  2. Richard C. Ready & Patricia A. Champ & Jennifer L. Lawton, 2010. "Using Respondent Uncertainty to Mitigate Hypothetical Bias in a Stated Choice Experiment," Land Economics, University of Wisconsin Press, vol. 86(2), pages 363-381.
  3. repec:ebl:ecbull:v:3:y:2004:i:6:p:1-13 is not listed on IDEAS
  4. Jayson L. Lusk & Ted C. Schroeder, 2004. "Are Choice Experiments Incentive Compatible? A Test with Quality Differentiated Beef Steaks," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 467-482.
  5. Joseph Little & Robert Berrens, 2004. "Explaining Disparities between Actual and Hypothetical Stated Values: Further Investigation Using Meta-Analysis," Economics Bulletin, AccessEcon, vol. 3(6), pages 1-13.
  6. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
  7. Scarpa, R. & Thiene, M. & Train, K., 2008. "Appendix to Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps," American Journal of Agricultural Economics Appendices, Agricultural and Applied Economics Association, vol. 90(4), November.
  8. Donna Dosman & Wiktor Adamowicz, 2006. "Combining Stated and Revealed Preference Data to Construct an Empirical Examination of Intrahousehold Bargaining," Review of Economics of the Household, Springer, vol. 4(1), pages 15-34, 03.
  9. Ricardo Scarpa & Mara Thiene & Kenneth Train, 2006. "Utility in WTP Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps," Working Papers in Economics 06/15, University of Waikato, Department of Economics.
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