A Latent-Segmentation Based Approach to Investigating the Spatial Transferability of Activity-Travel Models




Wafa, Zeina
Bhat, Chandra R.
Pendyala, Ram M.
Garikapati, Venu M.

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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 or criteria 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 paper proposes a latent segmentation approach whereby the most appropriate criteria for identifying areas with similar profiles are determined endogenously within the model estimation phase. In other words, the relationships embedded in the data set help identify the optimal set of criteria that can be used to cluster regions according to their similarity with respect to activity-travel characteristics of interest. The methodology is demonstrated and its efficacy established through a case study in this paper that utilizes the National Household Travel Survey (NHTS) data set. It is found that the methodology offers a robust mechanism for identifying latent segments and establishing criteria for assessing transferability of models between areas.


At the time of publication C.R Bhat and Z. Wafa were at the University of Texas at Austin, R.M Pendyala at the Georgia Institute of Technology, and V.M. Garikapato at Arizona State University.

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