(Georgia Institute of Technology, 2022-06)
Malokin, Aliaksandr; Mokhtarian, Patricia L.; Circella, Giovanni
Often in practice, the problem of unavailability of specific desired knowledge within one (“target”) dataset arises. However, if this knowledge can be extracted from a different (“source”) dataset and transferred between the datasets, this could increase the value of the target dataset at relatively minimal cost. The goal of this paper is to evaluate approaches to informing one dataset with knowledge from another and to evaluate the performance of the knowledge transferred into the target dataset. We use the 2009 National Household Travel Survey as the target dataset. The missing knowledge is transportation-related attitudes, whose inclusion could greatly improve travel behavior models. Our source dataset is obtained from the 2011–12 Multitasking Survey of Northern California Commuters. To achieve the goal, the set of common variables was first augmented with a large number of built-environment attributes. Then, after applying machine-learning methods, pro-transit, pro-active transportation, and pro-density attitudinal factor scores were predicted with the greatest precision; correlations of the predicted and observed scores were 0.564, 0.538, and 0.571, respectively. The performance of the transferred attitudes was measured by estimating linear regression models of vehicle ownership. The results showed that in the source dataset the observed attitudes account for an 8.0% model lift (improvement in goodness of fit), while in the target dataset the predicted attitudes account for a 1.2–5.4% model lift. Although these initial results are modest, we believe they show substantial promise, and the process has identified a number of opportunities for improvement and further research.
(Georgia Institute of Technology, 2021-08)
Kash, Gwen; Mokhtarian, Patricia L.; Circella, Giovanni
Through an extensive analysis of the Georgia subsample of the 2016–2017 National Household Travel Survey, this report provides an in-depth snapshot of the travel behavior of Georgians of all ages. It documents differences in travel needs and behavior by region and between demographic groups, focuses on measurement challenges and improved techniques, and identifies areas where future data collection is needed. In addition to an overview of key travel trends in the state, the report includes chapters on work travel; work flexibility (teleworking and flexible scheduling); new technologies and services, including alternative-fuel vehicles, shared mobility, and online shopping; social inclusion and equity; nonmotorized and access/egress travel; and travel for its own sake.