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A Utility Model for Travel Forecasting

Author

Listed:
  • Thomas F. Golob

    (General Motors Research Laboratories, Warren, Michigan)

  • Martin J. Beckmann

    (Brown University, Providence, Rhode Island)

Abstract

This paper shows how the problem of predicting traveler behavior may be approached in terms of economic utility theory. A general utility maximization model on the level of individual trip-making decisions is developed from basic hypotheses regarding the costs and benefits attributed to travel disaggregated into individual trips. For predictive purposes the form of the utility functions involved must be specified, and several possibilities are explored. The functional forms of the resulting travel demand functions are then derived. In order to pursue an analysis of variations in traveler behavior and in order to perform aggregation over individuals, disturbances in the parameters of the utility functions are considered. The parameters investigated are marginal utilities and the distributions used are normal distributions. In considering binary choices, a trip demand model, a modal split model, and a destination split model are obtained from the same basic methodology. Finally, possible approaches to testing the functional relations involved and to measuring marginal utilities are discussed.

Suggested Citation

  • Thomas F. Golob & Martin J. Beckmann, 1971. "A Utility Model for Travel Forecasting," Transportation Science, INFORMS, vol. 5(1), pages 79-90, February.
    • Handle: RePEc:inm:ortrsc:v:5:y:1971:i:1:p:79-90
    DOI: 10.1287/trsc.5.1.79
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    Cited by:

    1. Paas, Tiiu, 2002. "Gravity Approach for Exploring Baltic Sea Regional Integration in the Field of International Trade," Discussion Paper Series 26379, Hamburg Institute of International Economics.
    2. Lin, Ting (Grace) & Xia, Jianhong (Cecilia) & Robinson, Todd P. & Goulias, Konstadinos G. & Church, Richard L. & Olaru, Doina & Tapin, John & Han, Renlong, 2014. "Spatial analysis of access to and accessibility surrounding train stations: a case study of accessibility for the elderly in Perth, Western Australia," Journal of Transport Geography, Elsevier, vol. 39(C), pages 111-120.
    3. Wang, Fahui & Guldmann, Jean-Michel, 1996. "Simulating urban population density with a gravity-based model," Socio-Economic Planning Sciences, Elsevier, vol. 30(4), pages 245-256, December.
    4. Neoh, Jun Guan & Chipulu, Maxwell & Marshall, Alasdair & Tewkesbury, Adam, 2018. "How commuters’ motivations to drive relate to propensity to carpool: Evidence from the United Kingdom and the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 128-148.
    5. James B. Kau & C.F. Sirmans, 1979. "The Functional Form of the Gravity Model," International Regional Science Review, , vol. 4(2), pages 127-136, December.

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