Abstract:In an earlier report, we examined the constancy of travel time and money expenditure by reviewing the empirical evidence at both the aggregate and disaggregate levels (Chen and Mokhtarian, 1999). That report concluded that, although some regularities have been noted at the aggregate level, the considerable variation observed at the disaggregate level does not support the theory of a constant travel time budget. However, individual travel time and money expenditures may be influenced by a number of variables and hence be capable of being modeled with some degree of accuracy. The objective of this report is to explore different approaches to modeling individuals' time and money allocations to travel. Although the focus is on travel, it is important to consider activities as well, due to possible trade-offs between activities and travel. In this report, we consider three categories of activities: mandatory (e.g. paid work), maintenance (e.g., grocery shopping, medical appointments) and discretionary (e.g., social, recreational) activities. These three categories in general encompass all daily activities.

In this report, we consider the ideal study period to be relatively long – for example, a week or a month or even a year. This would hopefully allow us to capture activities and travel that people do not conduct on a daily basis. Examples include long distance business travel and vacation travel. In addition, in using a rather long study period, we avoid the situation where the amount of time allocated to a particular type of activity is zero.

In this report, we discuss five different approaches to disaggregate modeling of time and money allocations to travel and activities: four statistical estimation techniques, and the utility maximization framework within which each of the four statistical techniques may be applied. In Section 2, we present the single linear equation approach, which assumes a single endogenous variable. Where there is more than one endogenous variable, seemingly unrelated regression equations (SUR) or structural equations modeling, described in Section 3, are more appropriate. In Section 4, we discuss the application of linear and ordinal multinomial models to model relative desired mobility. Duration analysis, discussed in Section 5, may also be used to model individuals' time allocation behavior. In Section 6, we present the utility maximization framework and propose a modification of this approach as it has been developed to date. Data needs are discussed in Section 7. Discussion and conclusions come in Section 8.