01516766

Component

Forecasts by disaggregate travel demand models are often based on data from the most recent time point, even when cross-sectional data are available from multiple time points. However, this is not a good use of data. In his previous work, the author proposed a method for improving the forecasting performance of models by utilising not only the most recent dataset but also an older dataset. He assumes that the parameters are functions of time, which means that parameter values vary over time. He analysed journeys to work mode choice behaviours in Nagoya, Japan. 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. His models using data from three time points outperformed the model using only 1991 dataset. This paper extends the author's work by expressing parameters as functions of gross domestic product (GDP) per capita. Although functions of time ascribe all reasons of parameter changes to the trends of the times, functions of GDP per capita explain parameter changes as the effects of economic conditions. Functions of time have a difficulty for applying models to areas with different economic conditions; this problem is less serious for functions of GDP per capita. Since the GDP per capita has only positive domain, more flexible functional forms can be investigated. The author found that assuming parameters as functions of GDP per capita also outperforms the model using only 1991 dataset.

This paper was sponsored by TRB committee ADB40 Transportation Demand Forecasting.

01503729

14-2596

English

14p

2014

Transportation Research Board 93rd Annual Meeting

Digital/other

References; Tables

Data files; Forecasting; Gross domestic product; Mode choice; Travel demand; Work trips

Nagoya (Japan)

Highways; Planning and Forecasting; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2014 Paper #14-2596

TRIS, TRB, ATRI

Jan 27 2014 2:54PM