A trip generation model has been developed using a time-use perspective, in which trips are generated in conjunction with out-of-home activities, and time spent traveling is another component of overall time use. The model jointly forecasts three sets of endogenous variables- (1) activity participation and (2) travel time (together making up total out-of-home time use) and (3) trip generation – as a function of household characteristics and accessibility indices. It is estimated with data from the Portland, Oregon  1994 Activity and Travel Survey. Results show that the basic model, which has ten endogenous time use and trip generation variables and thirteen exogenous variables, fits well, and all postulated relationships are upheld. Test show that the basic model, which divides activities into work and nonwork, can be extended to a three-way breakdown of subsistence, discretionary and obligatory activities. The model can also capture the effects of in-home work on trip chaining and activity participation. We use the model to explore the effects on time use and trip chaining of GIS-based and zone-based accessibility indices.  

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.

Understanding attitudes held by the public about the acceptability, fairness, and effectiveness of congestion pricing systems is crucial to the planning and evaluation of such systems. In this study, joint models of attitude and behavior are developed to explain how both mode choice and attitudes regarding the San Diego I-15 Congestion Pricing Project differ across the population. Results show that some personal and situational explanations of opinions and perceptions are attributable to mode choices, but other explanations are independent of behavior. With respect to linkages between attitudes and behavior, none of the models tested found any significant effects of attitude on choice; all causal links were from behavior to attitudes.

The objective of this study was to evaluate the traffic safety of freeway High Occupancy Vehicle (HOV) lanes that are not separated by physical barriers from adjacent, general-purpose traffic lanes. The purpose of the HOV lanes is to offer an incentive to motorists to form carpools, thereby providing some relief to congestion attributable to the relatively high number of single-occupancy vehicles utilizing urban freeways. The research was aimed at determining the causes of any changes in safety conditions due to the operation of such lanes, and identifying possible actions to mitigate any adverse safety impacts.

The study focused on two HOV lane operations in the greater Los Angeles area: One is located on State Route 91 (SR-91, the Riverside Freeway) in Los Angeles County; the other is on State Route 55 (SR-55, the Costa Mesa/Newport Freeway) in Orange County. Both of these facilities have been implemented through partial removal of the left median shoulder and restriping of the lanes.