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

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.