L. Szolga

Human centered artificial intelligence for smart city traffic coordination

A human-centric edge intelligence framework that integrates federated reinforcement learning (FRL) with interpretable human–machine interfaces for urban traffic coordination and provides a practical pathway for evaluating intelligent mobility systems that balance decentralized optimization, privacy preservation, interpretability, scalability, and human-aligned operation is proposed.