Crowd analysis and management is a key area of study for transit agencies seeking to optimize their operations and to facilitate safety risk management activities. Key features of crowd analytics include passenger flow volume, crowd density, and walking speed. This study proposes a generalized artificial intelligence (AI)-based crowd analytics model framework for rail transit stations, by analyzing and visualizing crowd analysis data from video records of high-density crowds. Specifically, we propose a generalized AI-aided methodological framework (AI-Crowd) for calculating flow volume, crowd density, and walking speed. You Only Look Once (YOLO) and Deep SORT are integrated into the model framework to detect and track each individual’s dynamic position. Camera calibration is utilized to transform detected trajectories into a real-world coordinate system. Methods for calculating crowd dynamic metrics are formulated based on the data. To validate the model framework, several video records from a platform scenario at a major rail transit station are used. The model’s pedestrian counting accuracy can reach 95% and the fundamental diagrams of density–speed are shown to be consistent with empirical studies. Further crowd analysis of a stair scenario and a transferring passage scenario using the proposed model framework shows some differentiations in walking behavior. The methodology has further practical applications, such as monitoring social distancing.