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Mokhtarian,
Patricia L.
Mokhtarian,
Patricia L.
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ItemA Transfer Learning-Based Framework for Enriching National Household Travel Survey Data with Attitudinal Variables(Georgia Institute of Technology, 2022-06) Malokin, Aliaksandr ; Mokhtarian, Patricia L. ; Circella, Giovanni ; Georgia Institute of Technology. School of Civil and Environmental EngineeringOften 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.
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ItemAn Investigation of Methods for Imputing Attitudes from One Sample to Another( 2017-06) Malokin, Aliaksandr ; Mokhtarian, Patricia L. ; Circella, Giovanni ; Georgia Institute of Technology. School of Civil and Environmental EngineeringOften in practice, researchers have a (“target”) dataset that is desirable in many ways, but is missing some key variables, or “knowledge”, that would greatly enrich the value of the data for investigating questions of interest. If this knowledge could be extracted from a different but related (“source”) dataset and transferred between them by way of variables common to both datasets, it could improve the ability to perform analyses and increase the value of the dataset itself at a relatively minimal cost. In the current study, the target dataset comprises responses to the 2009 National Household Travel Survey (N ≈ 100,000), and the key missing variables are transportation-related attitudes, which could greatly improve the ability to predict travel behaviors. Our source dataset is obtained from the 2011-12 Multitasking Survey of Northern California Commuters (MSNCC, N ≈ 2000). To evaluate approaches to informing one dataset with knowledge from another and to eval-uate the performance of the knowledge transferred into the target dataset, we developed transfer learning and external validation frameworks, respectively. To implement the transfer learning framework, the set of common variables was first augmented by obtaining a large number of built and social environment characteristics linked to the residential locations of observations in each dataset. Then, applying machine-learning methods to the categorical and continuous attitudinal variables of the MSNCC, the LASSO (least absolute shrinkage and selection operator) regression learner showed the lowest generalization error over the 10 cross-validation folds in the context of the source dataset. The pro-transit, pro-active transportation, and pro-density attitudinal factor scores showed the greatest improvement over a naïve learner of assigning the average; correlations of the predicted and observed scores on these factors were 0.564, 0.538, and 0.571, respectively. The external validation framework was implemented by estimating vehicle ownership linear regression models, and comparing their goodness of fit with and without attitudes. The results showed that in the source dataset the observed attitudes account for an 8.0% model lift (i.e., improvement in goodness of fit), while in the target dataset the predicted attitudes account for a 1.2–5.4% model lift, depending on the extensiveness and nature of the variables used to impute them. 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.
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ItemAnalysis of the Georgia Add-on to the 2016-2017 National Household Travel Survey(Georgia Institute of Technology, 2021-08) Kash, Gwen ; Mokhtarian, Patricia L. ; Circella, Giovanni ; Georgia Institute of Technology. School of Civil and Environmental EngineeringThrough 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.