FedHTS: Federated Human-Centric Traffic System with Multimodal Adaptation and Standardized Evaluation

Intelligent Transportation Systems (ITS) face synergistic challenges of data privacy leakage, cross-regional adaptation barriers, insufficient human-centric design, and unstandardized performance assessment. To address these issues, this paper proposes FedHTS, a Federated Human-Centric Traffic System integrating multimodal intelligence and standardized evaluation. FedHTS leverages federated learning (FL) for privacy-preserving cross-regional collaboration, incorporates human-centric perception and interaction modules, and adopts standardized performance metrics. Specifically, we design a dynamic sparse federated learning framework to reduce communication overhead while supporting few-shot cross-regional adaptation. A multimodal decision engine fuses Neuro-VAE-Symbolic traffic dynamics modeling with context-gated spoken language understanding and zero-shot personalized recommendation. Human factors are integrated via VR-based control strategy simulation and driver state inference (informed by neural imaging insights). Performance is evaluated using super-efficiency SBM-DEA combined with neural network regression. Experiments on METR-LA and PEMS-BAY datasets demonstrate FedHTS outperforms baseline FL models by 18.7% in traffic prediction accuracy and reduces communication cost by 62.3%. Subjective evaluations via VR simulation show 89% user satisfaction with human- centric services. FedHTS provides a comprehensive solution for next-generation ITS by unifying privacy protection, cross-regional adaptation, and human-centric design.