Federated and Quantum-Inspired AI in Adaptive Traffic Systems Using Digital Twin Simulations and Predictive Analytics for Urban Flow Optimization and Carbon Footprint Reduction

Urban traffic congestion imposes severe economic and environmental burdens, necessitating privacy-preserving, scalable AI solutions for real-time optimization. This paper introduces a federated quantum-inspired AI framework integrated with digital twin simulations for adaptive traffic signal control and predictive urban flow management. Federated learning enables edge nodes traffic cameras and V2X units to collaboratively train congestion prediction models without sharing raw data, achieving GDPR/DPDP compliance while handling heterogeneous IoT streams. Quantum annealing-inspired optimizers solve multi-intersection signal timing as NP-hard Ising problems on classical hardware, exploring vast combinatorial spaces 5x faster than deep reinforcement learning baselines. Digital twins provide continuous virtual testing environments, fusing LiDAR/camera feeds with unscented Kalman filters, while hybrid LSTM-Transformer analytics forecast disruptions 15-60 minutes ahead with 92% accuracy. Extensive SUMO/CARLA simulations across Delhi-inspired grids demonstrate 28% congestion reduction, 22% CO2 emission cuts, and sub-200ms latency. Comparative evaluations confirm superior scalability and explainability, offering a deployable blueprint for Industry 5.0 smart cities targeting net-zero mobility.