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Predicting multi-faceted activity-travel adjustment strategies in response to possible congestion pricing scenarios using an Internet-based stated adaptation experiment

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  • Arentze, Theo
  • Hofman, Frank
  • Timmermans, Harry

Abstract

This paper reports the estimation of several discrete choice models describing reactions of individuals to congestion pricing scenarios. The models were estimated on data obtained in a stated adaptation experiment that was administered through the Internet and designed to examine how individuals adjust their activity-travel patterns. An activity-based approach is used meaning that all choice facets of activity patterns are taken into account as well as a complete set of activities. Estimates of price elasticities of travel demand are in line with other findings reported in the literature. Results of the stated adaptation experiment suggest that changing route or departure time is the most important way of adapting work trips, whereas public transport and working at home play a more limited role. For non-work activities changing route and switching to bike are the dominant responses.

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  • 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.
    • Handle: RePEc:eee:trapol:v:11:y:2004:i:1:p:31-41
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    Cited by:

    1. Cao, Xinyu & Mokhtarian, Patricia L., 2005. "How do individuals adapt their personal travel? A conceptual exploration of the consideration of travel-related strategies," University of California Transportation Center, Working Papers qt6357t1jj, University of California Transportation Center.
    2. Choo, Sangho & Mokhtarian, Patricia L, 2008. "How do people respond to congestion mitigation policies? A multivariate probit model of the individual consideration of three travel-related strategy bundles," University of California Transportation Center, Working Papers qt53s553tn, University of California Transportation Center.
    3. Cao, Xinyu & Mokhtarian, Patricia L., 2005. "How do individuals adapt their personal travel? A conceptual exploration of the consideration of travel-related strategies," Transport Policy, Elsevier, vol. 12(3), pages 199-206, May.
    4. Liao, Fanchao & Molin, Eric & Timmermans, Harry & van Wee, Bert, 2018. "The impact of business models on electric vehicle adoption: A latent transition analysis approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 531-546.
    5. Habib, Khandker Nurul & Sasic, Ana & Weis, Claude & Axhausen, Kay, 2013. "Investigating the nonlinear relationship between transportation system performance and daily activity–travel scheduling behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 342-357.
    6. Dogterom, Nico & Ettema, Dick & Dijst, Martin, 2018. "Behavioural effects of a tradable driving credit scheme: Results of an online stated adaptation experiment in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 52-64.
    7. Basil Schmid & Milos Balac & Kay W. Axhausen, 2019. "Post-Car World: data collection methods and response behavior in a multi-stage travel survey," Transportation, Springer, vol. 46(2), pages 425-492, April.
    8. Cao, Xinyu & Mokhtarian, Patricia L., 2005. "How do individuals adapt their personal travel? Objective and subjective influences on the consideration of travel-related strategies for San Francisco Bay Area commuters," University of California Transportation Center, Working Papers qt45k3391f, University of California Transportation Center.
    9. Dogterom, Nico & Ettema, Dick & Dijst, Martin, 2018. "Activity-travel adaptations in response to a tradable driving credits scheme," Transport Policy, Elsevier, vol. 72(C), pages 79-88.
    10. Le Vine, Scott & Adamou, Orestes & Polak, John, 2014. "Predicting new forms of activity/mobility patterns enabled by shared-mobility services through a needs-based stated-response method: Case study of grocery shopping," Transport Policy, Elsevier, vol. 32(C), pages 60-68.
    11. Saens, Rodrigo & Lobos, Germán, 2013. "Elasticidad precio de la demanda por autopistas interurbanas en Chile," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 79, pages 143-170, May.
    12. Koch, James V. & Zahedi, Ziniya, . "A Tale of Two Tunnels: Economic Eects of Newly Imposed Tolls on Heavily Traveled Tunnel Venues in Virginia," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 49(1).
    13. Sangho Choo & Patricia Mokhtarian, 2008. "How do people respond to congestion mitigation policies? A multivariate probit model of the individual consideration of three travel-related strategy bundles," Transportation, Springer, vol. 35(2), pages 145-163, March.
    14. Irene Casas & Mei‐Po Kwan, 2007. "The Impact of Real‐Time Information on Choices During the Commute Trip: Evidence from a Travel Simulator," Growth and Change, Wiley Blackwell, vol. 38(4), pages 523-543, December.
    15. Cao, Xinyu & Mokhtarian, Patricia L., 2005. "How do individuals adapt their personal travel? Objective and subjective influences on the consideration of travel-related strategies for San Francisco Bay Area commuters," Transport Policy, Elsevier, vol. 12(4), pages 291-302, July.
    16. Dujuan Yang & Gamze Dane & Harry J.P. Timmermans, 2013. "Mixture–amount experiments for measuring consumer preferences of energy-saving adaptation strategies: principles and illustration," Chapters, in: Thomas Vanoutrive & Ann Verhetsel (ed.), Smart Transport Networks, chapter 10, pages 208-223, Edward Elgar Publishing.

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