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A disaggregate Box-Cox Logit mode choice model of intercity passenger travel in Germany and its implications for high-speed rail demand forecasts

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  • Benedikt Mandel

    (Centre de recherche sur les transports, Université de Montréal, C.P. 6128, succursale A, Montréal, Canada H3C 3J7)

  • Marc Gaudry

    (Centre de recherche sur les transports, Université de Montréal, C.P. 6128, succursale A, Montréal, Canada H3C 3J7)

  • Werner Rothengatter

    (Institut für Wirtschaftspolitik und Wirtschaftsforschung der Universität Karlsruhe , Kollegium am Schloß, Bau IV, Postfach 6980, D-76128 Karlsruhe, Germany)

Abstract

We show that enriching logit mode choice model specification by mode attributes, socio economic variables and trip purpose characteristics significantly improves model quality, and that Box-Cox transformations applied to model attributes imply an asymmetry of the reaction curve, as well as more reasonable properties (diminishing marginal values of time savings, elasticities and values of time that differ among the modes) than those of the linear logit model. Moreover, the model yielded very different high speed rail market shares for Germany than results obtained with the usual linear utility functions.

Suggested Citation

  • Benedikt Mandel & Marc Gaudry & Werner Rothengatter, 1997. "A disaggregate Box-Cox Logit mode choice model of intercity passenger travel in Germany and its implications for high-speed rail demand forecasts," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 31(2), pages 99-120.
  • Handle: RePEc:spr:anresc:v:31:y:1997:i:2:p:99-120
    Note: Received: December 1996 / Accepted: April 1997
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    Citations

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    Cited by:

    1. Nicole Adler & Chris Nash & Eric Pels, 2008. "High-Speed Rail & Air Transport Competition," Tinbergen Institute Discussion Papers 08-103/3, Tinbergen Institute.
    2. Dutta, Goutam & Ghosh, Priyanko & Wanchoo, Arushi, 2013. "A Logarithmic Goal Programming Approach to Develop the Utility Function for Railway Travel," IIMA Working Papers WP2013-09-06, Indian Institute of Management Ahmedabad, Research and Publication Department.
    3. Frédéric Dobruszkes, 2011. "Airline and high-speed rail competition in Europe: towards a comeback of air transport?," ERSA conference papers ersa10p180, European Regional Science Association.
    4. Jasper Willigers & Han Floor & Bert Van Wee, 2005. "High-speed railÂ’s impact on the location of office employment within the Dutch Randstad area," ERSA conference papers ersa05p308, European Regional Science Association.
    5. Adler, Nicole & Pels, Eric & Nash, Chris, 2010. "High-speed rail and air transport competition: Game engineering as tool for cost-benefit analysis," Transportation Research Part B: Methodological, Elsevier, vol. 44(7), pages 812-833, August.
    6. Cascetta, Ennio & Coppola, Pierluigi, 2016. "Assessment of schedule-based and frequency-based assignment models for strategic and operational planning of high-speed rail services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 84(C), pages 93-108.
    7. Dobruszkes, Frédéric, 2011. "High-speed rail and air transport competition in Western Europe: A supply-oriented perspective," Transport Policy, Elsevier, vol. 18(6), pages 870-879, November.
    8. Bruzelius, Nils & Flyvbjerg, Bent & Rothengatter, Werner, 2002. "Big decisions, big risks. Improving accountability in mega projects," Transport Policy, Elsevier, vol. 9(2), pages 143-154, April.
    9. Espinosa-Aranda, José Luis & García-Ródenas, Ricardo & Ramírez-Flores, María del Carmen & López-García, María Luz & Angulo, Eusebio, 2015. "High-speed railway scheduling based on user preferences," European Journal of Operational Research, Elsevier, vol. 246(3), pages 772-786.

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