UC Irvine Institute of Transportation Studies

This research has two objectives. The first objective is to explore the use of the modeling tool called "latent structural equations" (structural equations with latent variables) in the general field of travel behavior analysis and the more specific field of dynamic analysis of travel behavior. The second objective is to apply a latent structural equation model in order to determine the causal relationships between income, car ownership, and mobility. 

Many transportation researchers might be unfamiliar with latent structural equation modeling, which is also known as "latent structural analysis," "causal analysis," and "soft modeling." However, most researchers will be quite familiar with techniques that are special cases of latent structural equations: e.g., conventional multiple regression and simultaneous equations, path analysis, and (confirmatory) factor analysis. Furthermore, recent advances in estimation techniques have made it possible to incorporate discrete choice variables and other non-normal variables in structural equations models. Thus, probit choice models (binomial, ordered, and multinomial) can be incorporated within the general model framework. 

The empirical analysis reported here involves dynamic travel demand data from the Dutch National Mobility Panel for the three years 1984 through 1986. All variables in the model, with the exception of income level in the first year, are endogenous: income is treated as an ordinal (four category) variable; car ownership is treated as either an ordinal (ordered probit) or a categorical (multinomial probit) choice variable; and mobility, in terms of car trips and public transport trips, is treated as two censored (tobit) continuous variables. The model fits the data well, but only scratches the surface of the potential of latent structural equation modeling with panel data. Some possible extensions are outlined.