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Forecasting demand for high speed rail

Author

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  • Börjesson, Maria

    (KTH)

Abstract

It is sometimes argued that standard state-of-practice logit based models cannot forecast the demand for substantially reduced travel times, for instance due to High Speed Rail (HSR). The present paper investigates this issue by reviewing travel time elasticities for long-distance rail travel in the literature and comparing these with elasticities observed when new HSR lines have opened. This paper also validates the Swedish official long-distance model and its forecasted demand for a proposed new HSR track, using aggregate data revealing how the air-rail modal split varies with the difference in generalized travel time between rail and air. The official linear-in-parameters long-distance model is also compared to a model applying Box-Cox transformations. The paper contributes to the empirical literature on long-distance travel, long-distance elasticities and HSR passenger demand forecasts. Results indicate that the Swedish state-of-practice model, and similar models, is indeed able to predict the demand for a HSR reasonably well. The non-linear model, however, has better model fit and slightly higher elasticities.

Suggested Citation

  • Börjesson, Maria, 2012. "Forecasting demand for high speed rail," Working papers in Transport Economics 2012:12, CTS - Centre for Transport Studies Stockholm (KTH and VTI).
  • Handle: RePEc:hhs:ctswps:2012_012
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    File URL: http://www.transportportal.se/SWoPEc/CTS2012-12.pdf
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    References listed on IDEAS

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    1. Roger Vickerman, 1997. "High-speed rail in Europe: experience and issues for future development," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 31(1), pages 21-38.
    2. Cheng, Yung-Hsiang, 2010. "High-speed rail in Taiwan: New experience and issues for future development," Transport Policy, Elsevier, vol. 17(2), pages 51-63, March.
    3. Börjesson, Maria & Eliasson, Jonas, 2014. "Experiences from the Swedish Value of Time study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 144-158.
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    Cited by:

    1. Marti-Henneberg, Jordi, 2015. "Attracting travellers to the high-speed train: a methodology for comparing potential demand between stations," Journal of Transport Geography, Elsevier, vol. 42(C), pages 145-156.

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    More about this item

    Keywords

    High speed rail; Travel demand; Forecasting; Air-rail share; Cost-benefit analysis;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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