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Linear or nonlinear utility functions in logit models? The impact on German high-speed rail demand forecasts

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  • Mandel, Benedikt
  • Gaudry, Marc
  • Rothengatter, Werner

Abstract

We have shown that nonlinearity of the representative utility functions of the Logit model applied to an intercity mode choice model implied the presence of asymmetry and of thresholds in the response curve, as well as results that had more reasonable properties (diminishing marginal values of time savings, elasticities and values of time that differed among the modes), and yielded very different high-speed rail market shares for Germany than results obtained with the usual linear utility functions.

Suggested Citation

  • Mandel, Benedikt & Gaudry, Marc & Rothengatter, Werner, 1994. "Linear or nonlinear utility functions in logit models? The impact on German high-speed rail demand forecasts," Transportation Research Part B: Methodological, Elsevier, vol. 28(2), pages 91-101, April.
    • Handle: RePEc:eee:transb:v:28:y:1994:i:2:p:91-101
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    Cited by:

    1. Kim, Kyung Min & Hong, Sung-Pil & Ko, Suk-Joon & Kim, Dowon, 2015. "Does crowding affect the path choice of metro passengers?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 292-304.
    2. Stathopoulos, Amanda & Hess, Stephane, 2012. "Revisiting reference point formation, gains–losses asymmetry and non-linear sensitivities with an emphasis on attribute specific treatment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1673-1689.
    3. Wiktor Budziński, 2015. "The effects of non-constant marginal utility of cost for public goods valuation," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 43.
    4. Bühler, Georg & Jochem, Patrick, 2008. "CO2 Emission Reduction in Freight Transports How to Stimulate Environmental Friendly Behaviour?," ZEW Discussion Papers 08-066, ZEW - Leibniz Centre for European Economic Research.
    5. Sung-Pil Hong & Kyung Min Kim & Suk-Joon Ko, 2021. "Estimating heterogeneous agent preferences by inverse optimization in a randomized nonatomic game," Annals of Operations Research, Springer, vol. 307(1), pages 207-228, December.
    6. Amanda Stathopoulos & Stephane Hess, 2011. "Referencing, Gains-Losses Asymmetry And Non-Linear Sensitivities In Commuter Decisions: One Size Does Not Fit All!," Working Papers 0511, CREI Università degli Studi Roma Tre, revised 2011.
    7. Jonas, Eliasson, 2000. "Time and income constraints in discrete choice models with an application to mode choice," MPRA Paper 95801, University Library of Munich, Germany.
    8. Christiaan Behrens & Eric Pels, 2009. "Intermodal Competition in The London-Paris Passenger Market: High-Speed Rail and Air Transport," Tinbergen Institute Discussion Papers 09-051/3, Tinbergen Institute.
    9. Marasco, A. & Picucci, A. & Romano, A., 2016. "Market share dynamics using Lotka–Volterra models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 49-62.
    10. Page, Kenneth & Pérez, Juan & Telha, Claudio & García-Echalar, Andrés & López-Ospina, Héctor, 2021. "Optimal bundle composition in competition for continuous attributes," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1168-1187.
    11. Jeppe Rich & Stefan L. Mabit, 2016. "Cost damping and functional form in transport models," Transportation, Springer, vol. 43(5), pages 889-912, September.
    12. Rich, Jeppe, 2020. "A spline function class suitable for demand models," Econometrics and Statistics, Elsevier, vol. 14(C), pages 24-37.
    13. Nakagawa, Dai & Hatoko, Masatoshi, 2007. "Reevaluation of Japanese high-speed rail construction: Recent situation of the north corridor Shinkansen and its way to completion," Transport Policy, Elsevier, vol. 14(2), pages 150-164, March.

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