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Microsimulation models that simulate travel demand at the level of individual travelers have been gaining increasing interest among practitioners. Transportation planning agencies across the country are steadily migrating to activity-based microsimulation models which provide considerable flexibility in testing policy scenarios. Generating a synthetic population is the first step in the application of any activity-based model system and hence has been a topic of extensive research in the activity-based modeling arena. Several researchers have developed population synthesizers that are able to generate synthetic populations while matching household-and personlevel constraints at a specified geographical resolution, e.g., census block group. However, information regarding control variables may not always be available at the specified spatial resolution. While information for some control variables may be available at the specified resolution, information on other control variables may be available only at a more aggregate spatial resolution. Ignoring control variables at different levels of spatial resolution could result in the generation of a synthetic population that is not representative of the underlying population. However, there has been limited progress on the development of synthetic population generators that are capable of accommodating control variables at multiple spatial resolutions. This paper proposes a robust approach to control for constraints at multiple geographic resolutions in generating a synthetic population. The methodology is an extension of the Iterative Proportional Updating (IPU) algorithm previously proposed and implemented by the authors. A case study demonstrating the efficacy of the enhanced algorithm is presented.

Modeling the interaction between the built environment and travel behavior is of much interest to transportation planning professionals due to the desire to curb vehicular travel demand through modifications to built environment attributes. However, such models need to take into account self-selection effects in residential location choice, wherein households choose to reside in neighborhoods and built environments that are conducive to their lifestyle preferences and attitudes. This phenomenon, well-recognized in the literature, calls for the specification and estimation of joint models of multi-dimensional land use and travel choice processes. However, the estimation of such model systems that explicitly account for the presence of unobserved factors that jointly impact multiple choice dimensions is extremely complex and computationally intensive. This paper presents a joint GEV-based logit regression model of residential location choice, vehicle count by type choice, and vehicle usage (vehicle miles of travel) using a copulabased framework that facilitates the estimation of joint equations systems with error dependence structures within a simple and flexible closed-form analytic framework. The model system is estimated on a sample derived from the 2000 San Francisco Bay Area Household Travel Survey. Estimation results show that there is significant dependency among the choice dimensions and that self-selection effects cannot be ignored when modeling land use travel behavior interactions.Keywords: land use and travel behavior, residential location choice, vehicle type choice, vehicle usage, vehicle miles of travel, joint model, copula-based approach, simultaneous equations model 3

Transportation models are currently unable to reflect adequately the impacts of policy and investment decisions on people's well-being and overall quality of life. This paper presents a multivariate ordered-response probit model that is able to capture the influence of activity-travel characteristics on subjective well-being while accounting for unobserved individual traits and attitudes that predispose people in relation to their emotional feelings.

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