Spatial transferability of travel forecasting models, or the ability to transfer models from one geographical region to another, can potentially help in significant cost and time savings for regions that cannot invest in extensive data-collection and model-development procedures. This issue is particularly important in the context of tour-based/activity-based models whose development typically involves significant data inputs, skilled staff, and long production times. However, most literature on model transferability has been in the context of traditionally used trip-based models, particularly for linear regression-based trip generation and logit-based mode choice models, with little evidence on the transferability of activity-based models and that of emerging model structures. The overarching goal of this dissertation is to assess the spatial transferability of activity-based travel demand models. To this end, the specific objectives are to: 1. Survey the literature to synthesize: (a) the approaches used to transfer models, (b) the metrics used to assess model transferability, (c) the available evidence on spatial transferability of travel models, and (d) notable gaps in literature; 2. Lay out a framework for assessing the spatial transferability of activity-based travel forecasting model systems, and evaluate alternative methods/metrics used for assessing the transferability of specific model components and their parameters; 3. Conduct empirical assessments of spatial transferability of the following two model components used in today's activity-based model systems: (a) daily activity participation and time-use models, and (b) tour-based time-of-day choice models. Data from the 2009 National Household Travel Survey (NHTS) and the 2000 San Francisco Bay Area Travel Survey (BATS) were used for these empirical assessments; 4. Conduct empirical assessments of model transferability using emerging model structures that have begun to be used in activity-based model systems - specifically the multiple discrete-continuous extreme value (MDCEV) model; 5. Investigate alternate ways of enhancing model transferability; specifically: (a) pooling data from different geographical regions, and (b) improvements to the model structure. The dissertation provides a framework for assessing the transferability of activity-based models systems, along with empirical evidence on the pros and cons of alternative methods and metrics of transferability assessment. The results suggest the need to consider model sensitivity to changes in explanatory variables as opposed to relying solely on the ability to predict aggregate distributions. Updating the constants of a transferred model using local data (a widely used method to transfer models) was found to help in increasing the model's ability to predict aggregate patterns but not necessarily in enhancing its sensitivity to changes in explanatory variables. Also, transferability assessments ought to consider sampling variance in parameter estimates as opposed to only the point estimates. Empirical analysis with the daily activity participation and time-use model shed new light on the prediction properties of the MDCEV model structure that have implications for model transferability. This led to the development of a new model structure called the multiple discrete continuous heteroscedastic extreme value (MDCHEV) model that incorporates heteroscedasticity in the model's stochastic distributions and helps in enhancing model transferability. Transferability assessment of the time-of-day choice models show encouraging evidence of transferability of a large proportion of the model coefficients, albeit except important parameters such as the travel time coefficients. Collectively, there is evidence that pooling data from multiple regions may help in building better transferable models than those transferred from a single region.

This thesis jointly models the 24 hour work trip timing and mode choice decisions of commuters in the Greater Toronto Area. A discrete-continuous specification, with a multinomial logit model for mode choice and an accelerated time hazard model for trip timing, is used to allow for unrestricted correlation between these two fundamental decisions. Statistically significant correlations are found between mode choice and trip timing for work journeys with expected differences between modes. Furthermore, the joint models have a wide range of policy sensitive statistically significant parameters of intuitive sign and magnitude, revealing expected differences between workers of different occupation groups. Furthermore, the estimated models have a high degree of fit to observed cumulative departure and arrival time distribution functions and to observed mode choices. Finally, sensitivity tests have demonstrated that the model is capable of capturing peak spreading in response to increasing auto congestion.

This thesis jointly models the 24 hour work trip timing and mode choice decisions of commuters in the Greater Toronto Area. A discrete-continuous specification, with a multinomial logit model for mode choice and an accelerated time hazard model for trip timing, is used to allow for unrestricted correlation between these two fundamental decisions. Statistically significant correlations are found between mode choice and trip timing for work journeys with expected differences between modes. Furthermore, the joint models have a wide range of policy sensitive statistically significant parameters of intuitive sign and magnitude, revealing expected differences between workers of different occupation groups. Furthermore, the estimated models have a high degree of fit to observed cumulative departure and arrival time distribution functions and to observed mode choices. Finally, sensitivity tests have demonstrated that the model is capable of capturing peak spreading in response to increasing auto congestion.