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The demand for intercity public transport: the case of business passengers

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  • Fredrik Carlsson

Abstract

Using a stated preference survey on business passengers travelling by rail or air between the two largest cities in Sweden, business passengers' preferences for different modes of transport and their corresponding attributes are investigated. Compared to what previous studies have found, the values put on attributes are very high, this including the environmental impact of the modes. This is largely explained by the fact that business passengers do not bear the cost of the trip, but still have the possibility of deciding the mode of transport. The usefulness of a more flexible specification of the demand for transport is explored using a random parameter logit model. It is found that there is a gain in using a random parameter model compared to a standard logit model, in the sense that it provides richer information about passengers' preferences. However, no consistent pattern is found in the differences in willingness to pay for attributes between the econometric specifications.

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  • Fredrik Carlsson, 2003. "The demand for intercity public transport: the case of business passengers," Applied Economics, Taylor & Francis Journals, vol. 35(1), pages 41-50.
    • Handle: RePEc:taf:applec:v:35:y:2003:i:1:p:41-50
    DOI: 10.1080/00036840210158921
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    Cited by:

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    2. Li, Binbin & Yao, Enjian & Yamamoto, Toshiyuki & Tang, Ying & Liu, Shasha, 2020. "Exploring behavioral heterogeneities of metro passenger’s travel plan choice under unplanned service disruption with uncertainty," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 294-306.
    3. Martine AUDIBERT & Yong HE & Jacky MATHONNAT, 2017. "What does demand heterogeneity tell us about health care provider choice in rural China?," Working Papers P193, FERDI.
    4. Yun Xiang & Chengcheng Xu & Weijie Yu & Shuyi Wang & Xuedong Hua & Wei Wang, 2019. "Investigating Dominant Trip Distance for Intercity Passenger Transport Mode Using Large-Scale Location-Based Service Data," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    5. Martine Audibert & Yong He & Jacky Mathonnat, 2020. "Dealing with Demand Heterogeneity on Health Care Provider Choice – The Case of Rural China," Asian Journal of Economics and Empirical Research, Asian Online Journal Publishing Group, vol. 7(2), pages 258-267.
    6. Ibeas, A. & dell’Olio, L. & Bordagaray, M. & Ortúzar, J. de D., 2014. "Modelling parking choices considering user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 41-49.
    7. Francisco Javier Amador Morera & Rosa Marina González Marrero, 2005. "Value of Travel Time Savings for University Students and Preference Heterogeneity," Hacienda Pública Española / Review of Public Economics, IEF, vol. 174(3), pages 25-41, September.
    8. Phanikumar, C.V. & Maitra, Bhargab, 2006. "Modeling Willingness-to-Pay Values for Rural Bus Attributes Under Different Trip Purposes," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 45(2).
    9. Martine AUDIBERT & Yong HE & Jacky MATHONNAT, 2017. "What does demand heterogeneity tell us about health care provider choice in rural China?," Working Papers P193, FERDI.
    10. Harris, Lloyd C. & Pressey, Andrew, 2021. "The myth of business tourist idiosyncrasy," Annals of Tourism Research, Elsevier, vol. 91(C).
    11. Alexandros Apostolakis & Shabbar Jaffry, 2005. "Heterogeneous Preferences for Greek Heritage Attractions," Tourism Economics, , vol. 11(2), pages 225-245, June.
    12. Martine Audibert & Yong He & Jacky Mathonnat, 2020. "Dealing with Demand Heterogeneity on Health Care Provider Choice –The Case of Rural China," Post-Print halshs-02963761, HAL.
    13. Martine Audibert & Yong He & Jacky Mathonnat, 2013. "Multinomial and Mixed Logit Modeling in the Presence of Heterogeneity: A Two-Period Comparison of Healthcare Provider Choice in Rural China," Working Papers halshs-00846085, HAL.
    14. Chakraborty, Rahul & Chakravarty, Sujoy, 2023. "Factors affecting acceptance of electric two-wheelers in India: A discrete choice survey," Transport Policy, Elsevier, vol. 132(C), pages 27-41.
    15. Yeonbae Kim & Jeong-Dong Lee & Daeyoung Koh, 2005. "Effects of consumer preferences on the convergence of mobile telecommunications devices," Applied Economics, Taylor & Francis Journals, vol. 37(7), pages 817-826.
    16. Caussade, Sebastián & Ortúzar, Juan de Dios & Rizzi, Luis I. & Hensher, David A., 2005. "Assessing the influence of design dimensions on stated choice experiment estimates," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 621-640, August.
    17. Bilbao Ubillos, J. & Fernández Sainz, A., 2004. "The influence of quality and price on the demand for urban transport: the case of university students," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(8), pages 607-614, October.
    18. Mesa-Arango, Rodrigo & Ukkusuri, Satish V., 2014. "Attributes driving the selection of trucking services and the quantification of the shipper’s willingness to pay," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 142-158.
    19. Martine Audibert & Yong He & Jacky Mathonnat, 2013. "Multinomial and Mixed Logit Modeling in the Presence of Heterogeneity: A Two-Period Comparison of Healthcare Provider Choice in Rural China," CERDI Working papers halshs-00846085, HAL.
    20. Unger, Orit & Uriely, Natan & Fuchs, Galia, 2016. "The business travel experience," Annals of Tourism Research, Elsevier, vol. 61(C), pages 142-156.

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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