Forecasting demand for high speed rail

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Maria Börjesson

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.

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