Active & Federated Learning & Explainable AI (XAI) represent a trio of advanced techniques in urban mobility artificial intelligence and machine learning, with focus on enhancing the efficiency of data labeling by selectively choosing data points that are most informative for improving a machine learning mode (Active Learning), addresses privacy concerns by allowing machine learning models to be trained collaboratively across decentralized edge devices or servers, preserving data privacy while leveraging diverse data sources in urban mobility applications (Federated Learning) and making machine learning models transparent and interpretable, even in spatiotemporal contexts, enhancing the understanding of AI-driven insights and decisions in the realm of urban mobility (Explainable AI).
