Prof. Zhao and MIT Transit Lab researchers showcase three sets of A.I. applications in public transportation: prediction, monitoring, and control. 1) Prediction: deep hybrid model with satellite images and graph embedded road networks for demand prediction in Chicago; 2) Monitoring: computer vision for bus travel time estimation in Boston and text mining for sentiment analysis in WMATA; and 3) RL-based bus operation control in CTA. They cover examples of unsupervised learning, supervised learning, and reinforcement learning and give a flavor of some future research: causal analysis with ML, generative AI, and multi-channel view of cities.