A latent-segmentation based approach to investigating the spatial transferability of activity-travel models

Spatial transferability of travel demand models has been an issue of considerable interest, particularly for small and medium sized planning areas that often do not have the resources and staff time to collect large scale travel survey data and estimate model components native to the region. With the advent of more sophisticated microsimulation-based activity-travel demand models, the interest in spatial transferability has surged in the recent past as smaller metropolitan planning organizations seek to take advantage of emerging modeling methods within the limited resources they can marshal. Traditional approaches to identifying geographical contexts that may borrow and transfer models between one another involve the exogenous a priori identification of a set of variables that are used to characterize the similarity between geographic regions. However, this ad hoc procedure presents considerable challenges as it is difficult to identify the most appropriate criteria a priori. To address this issue, this thesis proposes a latent segmentation approach whereby the most appropriate criteria for identifying areas with similar profiles are determined endogenously within the model estimation phase, customized for every model type. The end products are a set of optimal similarity measures that link regions to one another as well as a fully transferred model, segmented to account for heterogeneity in the population. The methodology is demonstrated and its efficacy established through a case study that utilizes the National Household Travel Survey (NHTS) dataset for information on weekday activities unemployed individuals within 9 regions in the states of California and Florida engage in. A multiple discrete continuous extreme value (MDCEV) model is developed that simulates the discrete nature of activity selection as well as the continuous nature of activity participation. The estimated model is then applied onto the Austin–San Marcos MSA, a context withheld from the original estimation in order to assess its performance. The performance of the segmented model was then examined vis-à-vis that of other models that are similar to the local region in only one dimension. It is found that the methodology offers a robust mechanism for identifying latent segments and establishing criteria for transferring models between areas.