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This project involves the use of machine learning methods to impute attitudes into the Georgia subsample of the 2016-17 National Household Travel Survey, training the algorithms on the responses to a 2017 attitudinal survey administered to a separate statewide sample in Georgia. The “common variables” needed to train the learning function will include socio-economic/demographic and other variables found in both samples, but will be augmented by (1) land use-related variables (obtained from multiple external sources) associated with respondents’ residential neighborhoods, and (2) (for the first time) lifestyle-oriented targeted marketing variables associated with the household/respondent that are purchased from a commercial provider. The project evaluates the effectiveness of targeted marketing variables for this purpose. The objectives of this project are (1) to impute attitudes into the Georgia subsample of the 2016-17 NHTS, training the imputation functions using attitudinally-rich data collected in Fall 2017 from a sample that is (reasonably) representative of the urban and small-town population of the state of Georgia; and (2) to augment the set of “common variables” available for training the imputation process with information from targeted marketing databases. Achievement of both objectives involves testing the efficacy of the imputed attitudes for predicting travel-related choices of interest, using a variety of comparisons.