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Travel demand forecasts improved by using cross-sectional data from multiple time points

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  • Nobuhiro Sanko

Abstract

Forecasts of travel demand are often based on data from the most recent time point, even when cross-sectional data is available from multiple time points. This is because forecasting models with similar contexts have higher transferability, and the context of the most recent time point is believed to be the most similar to the context of a future time point. In this paper, the author proposes a method for improving the forecasting performance of disaggregate travel demand models by utilising not only the most recent dataset but also an older dataset. The author assumes that the parameters are functions of time, which means that future parameter values can be forecast. These forecast parameters are then used for travel demand forecasting. This paper describes a case study of journeys to work mode choice analysis in Nagoya, Japan, using data collected in 1971, 1981, 1991, and 2001. Behaviours in 2001 are forecast using a model with only the most recent 1991 dataset and models that combine the 1971, 1981, and 1991 datasets. The models proposed by the author using data from three time points can provide better forecasts. This paper also discusses the functional forms for expressing parameter changes and questions the temporal transferability of not only alternative-specific constants but also level-of-service and socio-economic parameters. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:41:y:2014:i:4:p:673-695
    DOI: 10.1007/s11116-013-9464-7
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    References listed on IDEAS

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    1. Nobuhiro Sanko & Takayuki Morikawa, 2010. "Temporal transferability of updated alternative-specific constants in disaggregate mode choice models," Transportation, Springer, vol. 37(2), pages 203-219, March.
    2. L A Silman, 1981. "The Time Stability of a Modal-Split Model for Tel-Aviv," Environment and Planning A, , vol. 13(6), pages 751-762, June.
    3. 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.
    4. McCarthy, Patrick S., 1982. "Further evidence on the temporal stability of disaggregate travel demand models," Transportation Research Part B: Methodological, Elsevier, vol. 16(4), pages 263-278, August.
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    1. 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.
    2. Vij, Akshay & Gorripaty, Sreeta & Walker, Joan L., 2017. "From trend spotting to trend ’splaining: Understanding modal preference shifts in the San Francisco Bay Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 238-258.
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    5. Maruyama, Takuya & Fukahori, Tatsuya, 2020. "Households with every member out-of-home (HEMO): Comparison using the 1984, 1997, and 2012 household travel surveys in Kumamoto, Japan," Journal of Transport Geography, Elsevier, vol. 82(C).
    6. Rezaei, Ali & Patterson, Zachary, 2018. "Preference stability in household location choice: Using cross-sectional data from three censuses," Research in Transportation Economics, Elsevier, vol. 67(C), pages 44-53.

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