Latent-segmentation-based approach to investigating 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. Traditional approaches to identifying geographic contexts that may borrow and transfer models between one another involve the exogenous a priori identification of a set of variables used to characterize the similarity between geographic regions. However, this ad hoc procedure presents considerable challenges because it is difficult to identify the most appropriate criteria a priori. To address that issue, this paper proposes a latent segmentation approach: the most appropriate criteria for identifying areas with similar profiles are determined endogenously in the model estimation phase. The end products are a set of optimal criteria for clustering regions as well as a fully transferred model, segmented to account for heterogeneity in the population. The method is demonstrated, and its efficacy established through a case study in this paper that uses the National Household Travel Survey data set for information on weekday activities of nonworkers in nine regions in the states of California and Florida. The estimated model is then applied to a context withheld from the original estimation to assess its performance. The method is found to offer a robust mechanism for identifying latent segments and establishing criteria for transferring models between areas.