Advertisement

Transportation

, Volume 45, Issue 3, pp 905–918 | Cite as

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

  • Nobuhiro Sanko
Article
  • 267 Downloads

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.

Keywords

Repeated cross-sectional data Forecasting Transferability Parameter change Gross domestic product per capita Mode choice model 

Notes

Acknowledgements

This study was supported by JSPS KAKENHI Grant Numbers 25380564 and 16K03931. Comments from Andrew Daly, Kiyohito Utsunomiya, Mark Wardman, and David Watling are greatly appreciated. The author also acknowledges the use of data provided by the Chubu Regional Bureau, Japan’s Ministry of Land, Infrastructure, Transport and Tourism, and the NUTREND (Nagoya University TRansport and ENvironment Dynamics) Research Group. An earlier version of this paper was presented at the 93rd Annual Meeting of the Transportation Research Board in Washington, D.C., U.S.A., in January 2014. Comments from anonymous reviewers and suggestions from Tom van Vuren, Associate Editor, were appreciated.

References

  1. Abrantes, P.A.L., Wardman, M.R.: Meta-analysis of UK values of travel time: an update. Transp. Res. A 45(1), 1–17 (2011)CrossRefGoogle Scholar
  2. Cabinet Office, Government of Japan (2016). http://www.esri.cao.go.jp/. Accessed 19 Jan 2016
  3. Chamon, M., Mauro, P., Okawa, Y.: Mass car ownership in the emerging market giants. Econ. Policy 23(54), 243–296 (2008)CrossRefGoogle Scholar
  4. Dargay, J., Gately, D., Sommer, M.: Vehicle ownership and income growth, worldwide: 1960–2030. Energy J. 28(4), 143–170 (2007)CrossRefGoogle Scholar
  5. Fox, J., Daly, A., Hess, S., Miller, E.: Temporal transferability of models of mode-destination choice for the Greater Toronto and Hamilton Area. J. Transp. Land Use 7(2), 41–62 (2014)CrossRefGoogle Scholar
  6. Fox, J., Hess, S.: Review of evidence for temporal transferability of mode-destination models. Transp. Res. Rec. 2175, 74–83 (2010)CrossRefGoogle Scholar
  7. Garceau, T.J., Atkinson-Palombo, C., Garrick, N.: Peak travel and the decoupling of vehicle travel from the economy: a synthesis of the literature. Transp. Res. Rec. 2412, 41–48 (2014)CrossRefGoogle Scholar
  8. Kuhnimhof, T., Armoogum, J., Buehler, R., Dargay, J., Denstadli, J.M., Yamamoto, T.: Men shape a downward trend in car use among young adults—evidence from six industrialized countries. Transp. Rev. 32(6), 761–779 (2012)CrossRefGoogle Scholar
  9. Paulley, N., Balcombe, R., Mackett, R., Titheridge, H., Preston, J., Wardman, M., Shires, J., White, P.: The demand for public transport: the effects of fares, quality of service, income and car ownership. Transp. Policy 13(4), 295–306 (2006)CrossRefGoogle Scholar
  10. Portal Site of Official Statistics of Japan (2016). http://www.e-stat.go.jp/. Accessed 21 Jan 2016
  11. Sanko, N.: Travel demand forecasts improved by using cross-sectional data from multiple time points. Transportation 41(4), 673–695 (2014)CrossRefGoogle Scholar
  12. Sanko, N.: Factors affecting temporal changes in mode choice model parameters. Transp. Plann. Technol. 39(7), 641–652 (2016)CrossRefGoogle Scholar
  13. World Bank (2013). http://data.worldbank.org/country/japan. Accessed 17 Apr 2013

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Graduate School of Business AdministrationKobe UniversityKobeJapan

Personalised recommendations