Time is an important aspect of the activity patterns of individuals. An activity pattern can be described by means of a time-space diagram (Hagerstrand, 1970), that describes, for each moment within a given time interval, the location and type of activity of an individual. These time-space patterns are the result of various decisions and events experienced by that individual. In this paper, we will focus on the time dimensions of the space-time activity patterns of individuals. More specifically, we will focus attention on the allocation of time to a number of out-of-home activities. Other aspects, such as the timing and scheduling of activities are outside the scope of this paper.

The first question addressed in this research is: how is fuel type choice related to car mobility measured, where mobility is measured in terms of overall usage (kilometers per year) and commuting distance? Causality can be anticipated in both directions: a high travel demand might explain the purchase of a car with lower fuel costs, but the ownership of such a car might result in more travel. The second question is: what are the influences of commuting subsidies, public transport season tickets, income and other background sociodemographic variables on fuel type choice and car mobility? 

A joint continuous/discrete choice demand model is specified in terms of a set of dynamic simultaneous equations. The endogenous (dependent) variables are car fuel type, car usage, and commuting distance, each measured at two points in time. Car fuel type is treated as a three category discrete variable ordered in terms of fuel cost; usage is a continuous variable; and commuting distance is a censored continuous variable (having the censoring value zero for households with no workers outside the home location). The model is restricted to single-car households, and is estimated on a pooled sample of the Dutch National Mobility Panel for the years 1984-1988. Elasticities are calculated for each endogenous variable as a function of the other endogenous variables and certain exogenous variables.

In this paper a simultaneous modeling system for dichotomous endogenous variables is developed and applied empirically to longitudinal travel demand data of modal choice. The reported research is motivated by three factors. First, the analysis of discrete data has become standard practice among geographers, sociologists, and economists. In the seventies a number of new tools were developed to handle multivariate discrete data (Bishop, et al., 1975; Fienberg, 1980; Goodman, 1972). However, while these methods are invaluable in studying empirical relationships among sets of discrete variables, they have a limited ability to reveal the underlying causal structure that generated the data. 

Second, in travel demand analysis and housing market modeling, attention has been focused largely on single-equation models. It can be argued that this scope is too limited. Human decisions are usually not taken in isolation but in conjunction with other decisions and events. There may be complex feedback relations, recursive, sequential, and simultaneous decision structures that cannot be adequately described in a single equation. This has been a major motivation in the seventies in sociology for the development of a new modeling approach: linear structural equations with latent variables. Such models combine the classical simultaneous equation system model with a linear measurement model. Original developments, particularly the LISREL model (Jtireskog, 1973, 1977), did not allow for discrete dependent variables. More recently, Muthen (1983, 1984, 1987) and others (e.g., Bentler, 1983, 1985) developed models that incorporate various types of non-normal endogenous variables, including censored/truncated polytomous and dummy variables. This paper explores the possibilities of this method for simultaneous equation models in dynamic analysis of mobility. 

A third motivation for the present research is the rapid growth of longitudinal data sets. In recent years many longitudinal surveys have become available for geographical, economic, and transportation analyses. In labor and housing market analysis the Panel Study of Income Dynamics (PSID, 1984) has played an important role (Heckman and Singer, 1985; Davies and Crouchley, 1984, 1985). In consumer behavior, the Cardiff Consumer Panel has been a major motivation for the development and testing of dynamic discrete choice models (Wrigley, et al., 1985; Wrigley and Dunn, 1984a, 1984b, 1984c, 1985; Dunn and Wrigley, 1985; Uncles, 1987). In the Netherlands a large general mobility panel has been conducted annually since 1984 (J. Golob, et al., 1985; van Wissen and Meurs, 1989). Here analyses have focused on discrete data on modal choice (T. Golob, et al., 1986), as well as on dynamic structural modeling (Golob and Meurs, 1987, 1988; Kitamura, 1987; Golob and van Wissen, 1988; Golob, 1988). The present paper is an extension of this line of research to incorporate dynamic structural models of modal choice, using data from the Dutch Mobility Panel.