Forecasting demand for high speed rail |
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| Institution: | 1. Dept. of Economics and Management, University of Trento, Italy;2. Dept. of Econometrics, Statistics and Applied Economics, University of Barcelona, Spain;1. University of Lyon, Transport Urban Planning Economics Laboratory (LAET), ISH, 14 Avenue Berthelot, 69 365 Lyon, Cedex 07, France;2. Ministry of Infrastructures and Transport, General Direction for Rail and Marine Investigations, Viale dell’Arte 16, 00144 Roma, Italy;1. Sousse National School of Engineering, Networked Objects Control Communication Systems Lab (NOCCS), Tunisia;2. Mines ParisTech - PSL Research University, Centre de Recherche en Informatique (CRI), France;3. Sorbonne University, CNRS, Laboratoire d’Informatique de Paris 6 (LIP6), France;4. Electronics and Microelectronics Laboratory, Faculty of Sciences, University of Monastir (FSM), Tunisia;1. Engineering Systems, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E40-246, Cambridge, MA 02139, USA;2. Civil and Environmental Engineering and Engineering Systems, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 1-163, Cambridge, MA 02139, USA;3. Aeronautics and Astronautics and Engineering Systems, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 33-328, Cambridge, MA 02139, USA;1. Department of Transportation Engeneering - University of Naples “Federico II”, Via Claudio 21- 80125 Napoli (Italy);2. Department of of Enterprise Engineering University of Rome “Tor Vergata”, Via del Politecnico 1, Roma 00133, Italy |
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| 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 the literature on travel time elasticities for long distance rail travel and comparing these with elasticities observed when new HSR lines have opened. This paper also validates the Swedish long distance model, Sampers, and its forecast demand for a proposed new HSR, using aggregate data revealing how the air–rail modal split varies with the difference in generalized travel time between rail and air. The Sampers long distance model is also compared to a newly developed 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 Sampers model is indeed able to predict the demand for HSR reasonably well. The new non-linear model has even better model fit and also slightly higher elasticities. |
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| Keywords: | High speed rail Demand Forecasting Air–rail share Cost–benefit analysis Box–Cox transformation of travel time |
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