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A utility maximizing model of the demand for multi-destination non-work travel

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  • Horowitz, Joel

Abstract

In this paper, a structural model of the demand for multi-destination non-work travel is developed, and an empirical illustration of the structural model is presented. The structural model is based on the principle of utility maximization and differs from previous utility maximizing models of multi-destination non-work travel in several important ways. In contrast to most previous models, the model presented here incorporates travel frequency, destination choice and mode choice for both single and multi-destination travel into a unified utility-maximizing framework. The model includes a representation of the demand for travel between individual origin-destination pairs but avoids the need for enumerating complete travel patterns. Finally, the model incorporates the concept that current travel decisions depend on past travel decisions and future travel plans, as well as on current conditions. Empirical tests of the model have produced encouraging results concerning the model's structural validity. The empirical tests also have indicated that there is a need to develop improved sets of explanatory variables for non-work travel.

Suggested Citation

  • Horowitz, Joel, 1980. "A utility maximizing model of the demand for multi-destination non-work travel," Transportation Research Part B: Methodological, Elsevier, vol. 14(4), pages 369-386, December.
    • Handle: RePEc:eee:transb:v:14:y:1980:i:4:p:369-386
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    Citations

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    Cited by:

    1. R G V Baker, 1996. "Multipurpose Shopping Behaviour at Planned Suburban Shopping Centres: A Space—Time Analysis," Environment and Planning A, , vol. 28(4), pages 611-630, April.
    2. Brooks, Charles M. & Kaufmann, Patrick J. & Lichtenstein, Donald R., 2008. "Trip chaining behavior in multi-destination shopping trips: A field experiment and laboratory replication," Journal of Retailing, Elsevier, vol. 84(1), pages 29-38.
    3. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90260, University Library of Munich, Germany.
    4. Bowman, J. L. & Ben-Akiva, M. E., 2001. "Activity-based disaggregate travel demand model system with activity schedules," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 1-28, January.
    5. Jiri Horak & Jan Tesla & David Fojtik & Vit Vozenilek, 2019. "Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    6. Javier D. Donna, 2021. "Measuring long‐run gasoline price elasticities in urban travel demand," RAND Journal of Economics, RAND Corporation, vol. 52(4), pages 945-994, December.
    7. Fischer, Manfred M. & Nijkamp, Peter, 1987. "From static towards dynamic discrete choice modelling : A State of the Art Review," Regional Science and Urban Economics, Elsevier, vol. 17(1), pages 3-27, February.
    8. Han Xu & Jon C. Lovett & Rob Law, 2022. "Understanding Destination Value Co-Creation on Social Media: An Application of Travel Blog Analysis," Tourism and Hospitality, MDPI, vol. 3(3), pages 1-16, June.
    9. Martínez, L. Miguel & Viegas, José Manuel, 2013. "A new approach to modelling distance-decay functions for accessibility assessment in transport studies," Journal of Transport Geography, Elsevier, vol. 26(C), pages 87-96.
    10. Bhat, Chandra R., 1997. "Work travel mode choice and number of non-work commute stops," Transportation Research Part B: Methodological, Elsevier, vol. 31(1), pages 41-54, February.

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