The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people's lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium-and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology.Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the "true" causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.

This paper presents an examination of the significance of residential sorting or self selection effects in understanding the impacts of the built environment on travel choices. Land use and transportation system attributes are often treated as exogenous variables in models of travel behavior. Such models ignore the potential self selection processes that may be at play wherein households and individuals choose to locate in areas or built environments that are consistent with their lifestyle and transportation preferences, attitudes, and values. In this paper, a simultaneous model of residential location choice and commute mode choice that accounts for both observed and unobserved taste variations that may contribute to residential self selection is estimated on a survey sample extracted from the 2000 San Francisco Bay Area household travel survey. Model results show that both observed and unobserved residential self selection effects do exist; however, even after accounting for these effects, it is found that built environment attributes can indeed significantly impact commute mode choice behavior.The paper concludes with a discussion of the implications of the model findings for policy planning.

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This study presents a joint model system of residential location and activity time-use choices that considers a comprehensive set of activity-travel environment (ATE) variables, as well as sociodemographic variables, as determinants of individual weekday activity time-use choices. The model system takes the form of a joint mixed Multinomial Logit-Multiple DiscreteContinuous Extreme Value (MNL-MDCEV) structure that (a) accommodates differential sensitivity to the ATE attributes due to both observed and unobserved individual-related attributes, and (b) controls for the self selection of individuals into neighborhoods due to both observed and unobserved individual-related factors. The joint model system is estimated on a sample of 2793 households and individuals residing in Alameda County in the San Francisco Bay Area.The model results indicate the significant presence of residential self-selection effects due to both observed and unobserved individual-related factors. For instance, individuals from households with more bicycles are associated with a higher preference for out-of-home physically active pure recreational travel pursuits (such as bicycling around in the neighborhood). These same individuals locate into neighborhoods with good bicycling facilities. This leads to a non-causal association between individuals' time investment in out-of-home physically active pure recreational travel and bicycling facilities in their residential neighborhoods. Thus, ignoring the effect of bicycle ownership in the time-use model, would lead to an inflated estimate of the effect of bicycling facility density on the time invested in physically active pure recreational travel. Similarly, there are significant unobserved individual factors that lead to a high preference for physically active recreational activities and also make individuals locate in areas with good bicycling facilities. When such unobserved factors were controlled by the proposed joint residential location and time-use model, the impact of bicycling facility density on out-of-home physically active recreational activities ceased to be statistically significant (from being statistically significant in the independent time-use model). These results highlight the need to control for residential self-selection effects when estimating the effects of the activity-travel environment on activity time-use choices.Keywords: Residential location choice, residential self-selection, time-use, multiple discretecontinuous extreme value model, integrated land use-transportation modeling. 1 INTRODUCTIONThe primary focus of transportation planning, until the past three decades or so, was to meet long-term mobility needs by providing adequate transportation infrastructure supply. In such a mobility-centric, supply-oriented, planning process, the main role of travel demand models was to predict aggregate travel demand for long-term socio-economic scenarios, and for alternative transportation system characteristics and land-use configurations.Over the past three decades, h...

This paper develops a multiple discrete-continuous nested extreme value (MDCNEV) model that relaxes the independently distributed (or uncorrelated) error terms assumption of the multiple discrete-continuous extreme value (MDCEV) model proposed by Bhat (2005 and. The MDCNEV model captures inter-alternative correlations among alternatives in mutually exclusive subsets (or nests) of the choice set, while maintaining the closed-form of probability expressions for any (and all) consumption pattern(s).The MDCNEV model is applied to analyze non-worker out of home discretionary activity time-use and activity timing decisions on weekdays using data from the 2000 San Francisco Bay Area data. This empirical application contributes to the literature on activity timeuse and activity timing analysis by considering daily activity time-use behavior and activity timing preferences in a unified utility maximization-based framework. The model estimation results provide several insights into the determinants of non-workers' activity time-use and timing decisions. The MDCNEV model performs better than the MDCEV model in terms of goodness of fit. However, the nesting parameters are very close to 1, indicating low levels of correlation. Nonetheless, even with such low correlation levels, empirical policy simulations indicate non-negligible differences in policy predictions and substitution patterns exhibited by the two models. Experiments conducted using simulated data also corroborate this result.