Smart City Gnosys
Smart city article details
| Title | Dt-Ctfp: 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction |
|---|---|
| ID_Doc | 21127 |
| Authors | Wu B.; Zhang J.; Yuan J.; Zeng Y.; Zhan P.; Yin Y.; Wan J.; Gao H. |
| Year | 2025 |
| Published | IEEE Transactions on Intelligent Transportation Systems |
| DOI | http://dx.doi.org/10.1109/TITS.2025.3582356 |
| Abstract | In the era of big data, intelligent transportation systems are crucial for the development of smart cities, significantly impacting urban economic growth and planning. The integration of 6G networks and digital twin technology presents unprecedented opportunities to enhance urban traffic management through real-time data synchronization and high-fidelity simulations. Accurate traffic flow prediction is vital for congestion control, intelligent route planning, and effective urban traffic management. However, existing deep learning models often struggle to capture the complex spatio-temporal dependencies and dynamic spatial relationships inherent in urban traffic data, particularly in data-scarce environments. Given the spatial heterogeneity of urban data, where dense and sparse regions coexist, improving prediction accuracy in sparse areas is critical to ensuring overall forecasting performance. To address these challenges, we propose a novel framework called 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction (DT-CTFP), which integrates advanced deep learning models within a 6G-supported digital twin environment. The framework leverages real-time data processing capabilities and ultra-low latency of 6G networks to capture complex traffic features and dynamic spatial dependencies. In data-rich regions, the Dynamic Graph Multi-Attention (DGMA) model is used to learn fine-grained spatio-temporal patterns, while for data-scarce regions, the Cross-Area Transfer Prediction (CATP) model utilizes meta-learning techniques to transfer knowledge from data-rich urban areas, improving prediction accuracy in areas with limited data. Experimental results demonstrate the superiority of the DT-CTFP framework, achieving up to 6% reductions in RMSE and 4% reductions in MAE across multiple datasets, highlighting its enhanced prediction accuracy and efficiency. These results emphasize the framework’s capacity to improve traffic management and vehicle-road cooperation within a digital twin smart city. © 2000-2011 IEEE. |
| Author Keywords | 6G networks; cross-area knowledge transfer; digital twin; dynamic graph multi-attention; intelligent transportation systems; smart city; traffic flow prediction |
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