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Travel demand forecasts improved by using cross-sectional data from multiple time points: enhancing their quality by linkage to gross domestic product

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  • Nobuhiro Sanko

    (Kobe University)

Abstract

Forecasts using disaggregate travel demand models are often based on data from the most recent time point, even when cross-sectional data is available from multiple time points. However, this is not a good use of the data. In a previous study, the author proposed a method that jointly utilises cross-sectional data from multiple time points in which parameters are assumed to be functions of time (year), meaning that the parameter values vary over time. The method was applied to journey-to-work mode choice analyses for Nagoya, Japan. Behaviours in 2001 were forecast using one model with only the most recent 1991 dataset and other models that combined datasets for 1971, 1981, and 1991. The latter models outperformed the former model, which demonstrated the applicability of the proposed method. Although the functions of time ascribe the parameter changes to the trends over time, the theoretical underpinnings needed further investigation. The aim of this study is to analyse the same dataset used in the author’s previous study, but express the parameters as functions of gross domestic product (GDP) per capita. This method has fewer problems related to its theoretical underpinnings, since the parameter changes are explained by the effects of economic conditions. The functions of GDP per capita produced better forecasts than the functions of time. In addition, the functions of GDP per capita present fewer problems when choosing functional forms and extrapolating into the distant future/past and even to other areas. Sensitivity analysis with respect to uncertainties in the future GDP per capita showed that the proposed models are practical.

Suggested Citation

  • Nobuhiro Sanko, 2018. "Travel demand forecasts improved by using cross-sectional data from multiple time points: enhancing their quality by linkage to gross domestic product," Transportation, Springer, vol. 45(3), pages 905-918, May.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:3:d:10.1007_s11116-016-9755-x
    DOI: 10.1007/s11116-016-9755-x
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    References listed on IDEAS

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    1. Tobias Kuhnimhof & Jimmy Armoogum & Ralph Buehler & Joyce Dargay & Jon Martin Denstadli & Toshiyuki Yamamoto, 2012. "Men Shape a Downward Trend in Car Use among Young Adults—Evidence from Six Industrialized Countries," Transport Reviews, Taylor & Francis Journals, vol. 32(6), pages 761-779, September.
    2. Paulley, Neil & Balcombe, Richard & Mackett, Roger & Titheridge, Helena & Preston, John & Wardman, Mark & Shires, Jeremy & White, Peter, 2006. "The demand for public transport: The effects of fares, quality of service, income and car ownership," Transport Policy, Elsevier, vol. 13(4), pages 295-306, July.
    3. Fox, James & Daly, Andrew & Hess, Stephane & Miller, Eric, 2014. "Temporal transferability of models of mode-destination choice for the Greater Toronto and Hamilton Area," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 41-62.
    4. Joyce Dargay & Dermot Gately & Martin Sommer, 2007. "Vehicle Ownership and Income Growth, Worldwide: 1960-2030," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 143-170.
    5. Nobuhiro Sanko, 2014. "Travel demand forecasts improved by using cross-sectional data from multiple time points," Transportation, Springer, vol. 41(4), pages 673-695, July.
    6. Abrantes, Pedro A.L. & Wardman, Mark R., 2011. "Meta-analysis of UK values of travel time: An update," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(1), pages 1-17, January.
    7. Nobuhiro Sanko, 2016. "Factors affecting temporal changes in mode choice model parameters," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(7), pages 641-652, October.
    8. Marcos Chamon & Paolo Mauro & Yohei Okawa, 2008. "Mass car ownership in the emerging market giants [‘Petroleum taxes’]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 23(54), pages 244-296.
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