Data-driven mean-field-type game theory aims to incorporate certain data sets and real measurements into MFTG settings in a closed-loop fashion. Researchers use the measured data to learn and extract additional useful information from the field which the model then takes into consideration. One can thus utilize the model to study emerging dynamics and features and conduct new measurements, intervention measures, and actions. Scientists have developed mean-field-type filters, mean-field-type forecasting, and risk-aware filtering and forecasting based on MFTG theory; they apply these techniques to intelligent transportation systems.