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Discrete choice models of traveler participation in differential time of day pricing programs

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  • Burris, Mark W.
  • Pendyala, Ram M.

Abstract

Tolls that vary based on time of day or congestion are gaining attention around the world as a potential travel demand management strategy that can shift peak period travel to off peak periods thereby contributing to peak period congestion relief. However, despite the widespread interest in the concept, there is very little empirical data available on the impacts of variable tolls on traveler choices and disaggregate models that can be used to predict traveler response to variable pricing are few. This paper reports on results from two bridges with differential time of day tolls in the Lee County area of Florida in the United States. Using travel survey data collected at these two bridges, discrete choice models of traveler response to the variable toll rates are estimated. The models indicate that travelers who are retired, have a low income, have flextime at their place of employment, or have a flexible travel schedule are more likely to alter their time of travel with greater frequency due to the variable toll.

Suggested Citation

  • Burris, Mark W. & Pendyala, Ram M., 2002. "Discrete choice models of traveler participation in differential time of day pricing programs," Transport Policy, Elsevier, vol. 9(3), pages 241-251, July.
  • Handle: RePEc:eee:trapol:v:9:y:2002:i:3:p:241-251
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    Cited by:

    1. Burris, Mark W. & Figueroa, Carlos F., 2006. "Analysis of Traveler Characteristics by Mode Choice in HOT Corridors," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 45(2).
    2. Fei Yang & Lin Chen & Yang Cheng & Zhenxing Yao & Xu Zhang, 2015. "Urban public transport choice behavior analysis and service improvement policy-making: a case study from the metropolitan city, Chengdu, China," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 806-816, April.
    3. Stopher, Peter R., 2004. "Reducing road congestion: a reality check," Transport Policy, Elsevier, vol. 11(2), pages 117-131, April.
    4. Woo, C.K. & Cheng, Y.S. & Li, R. & Shiu, A. & Ho, S.T. & Horowitz, I., 2015. "Can Hong Kong price-manage its cross-harbor-tunnel congestion?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 94-109.
    5. Holguín-Veras, José & Allen, Brandon, 2013. "Time of day pricing and its multi-dimensional impacts: A stated preference analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 55(C), pages 12-26.
    6. Janson, Michael & Levinson, David, 2014. "HOT or not," Research in Transportation Economics, Elsevier, vol. 44(C), pages 21-32.
    7. António Menezes & José Vieira, 2006. "Willingness to Pay for Airline Services Attributes: Microeconometric Evidence from a Stated Preference Discrete Choice Model," EcoMod2006 272100063, EcoMod.
    8. Kockelman, Kara M. & Kalmanje, Sukumar, 2005. "Credit-based congestion pricing: a policy proposal and the public's response," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(7-9), pages 671-690.
    9. José Holguín-Veras & Iván Sánchez-Díaz & Benjamin Reim, 2016. "ETC adoption, time-of-travel choice, and comprehensive policies to enhance time-of-day pricing: a stated preference investigation," Transportation, Springer, vol. 43(2), pages 273-299, March.
    10. José Holguín-Veras & Qian Wang & Ning Xu & Kaan Ozbay, 2011. "The impacts of time of day pricing on car user behavior: findings from the Port Authority of New York and New Jersey’s initiative," Transportation, Springer, vol. 38(3), pages 427-443, May.
    11. Konstadinos G. Goulias & Ram M. Pendyala, 2014. "Choice context," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 5, pages 101-130, Edward Elgar Publishing.
    12. Xie, Litian & Olszewski, Piotr, 2011. "Modelling the effects of road pricing on traffic using ERP traffic data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(6), pages 512-522, July.
    13. Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2004. "Predicting multi-faceted activity-travel adjustment strategies in response to possible congestion pricing scenarios using an Internet-based stated adaptation experiment," Transport Policy, Elsevier, vol. 11(1), pages 31-41, January.
    14. Tillema, Taede & Ben-Elia, Eran & Ettema, Dick & van Delden, Janet, 2013. "Charging versus rewarding: A comparison of road-pricing and rewarding peak avoidance in the Netherlands," Transport Policy, Elsevier, vol. 26(C), pages 4-14.

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