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This paper focuses on the problem of model transferability for machine learning models used to estimate hourly traffic volumes. The presented findings enable not only an increase in the accuracy of existing models but also, simultaneously, reduce the cost of data needed for training the models—making statewide traffic volume estimation more economically feasible. Previous research indicates that machine learning volume estimation models that leverage GPS probe data can provide transportation agencies with accurate estimates of hourly traffic volumes—which are fundamental for both operational and planning purposes—and do so with a higher level of accuracy than the prevailing profiling method. However, this approach requires a large dataset for model calibration (i.e., input and continuous count station data), which involves significant monetary investment and data-processing effort. This paper proposes solutions, which allow the model to be prepared using a much smaller dataset, given that a previously collected dataset, which may be gathered in a different place and time period, exists. Based on a broad selection of experiments, the results indicate that the proposed approach is capable of achieving similar model performance while collecting data for a 5 times shorter time period and utilizing 1/4 of the number of continuous count stations. These findings will help reduce the cost of preparing and maintaining the traffic volume models and render the traffic volume estimations more financially appealing. [ABSTRACT FROM AUTHOR]