This paper introduces hybrid AI models for structured mobility prediction in metropolitan areas, focusing on Vienna, to guide citizens toward greener transportation options. The AI-CENTIVE project explores how AI can identify effective incentives by forecasting future trips using a combination of traditional machine learning and modern deep learning architectures. Trained on a dataset of commuter trips from the Ummadum app, the models predict transport mode, time, origin, destination, distance, and duration. The most accurate predictions trigger notifications suggesting sustainable alternatives. The evaluation of various hybrid architectures revealed that a graph convolutional network that uses statistical patterns achieved the best performance on the analyzed dataset. The presented research contributes to leveraging AI to promote sustainable mobility through targeted incentivization.