A Real-Time Urban Traffic Congestion Prediction Framework Based on Dynamic Risk Field and Multi-Source Data Fusion

This paper proposes a novel real-time traffic congestion prediction framework based on dynamic risk field modeling and multi-source data fusion, tailored for complex urban road networks. By integrating physically interpretable risk field theory with data-driven deep learning methods, we develop a hybrid system that quantifies spatiotemporal congestion risk in a transparent and computationally efficient manner. The proposed model introduces a dynamic field superposition structure driven by Gaussian kernel functions, enabling intuitive three-dimensional visualization of congestion propagation. To meet the demands of real-time urban mobility management, we design a spatial parallel processing architecture that significantly improves computational speed while maintaining high prediction accuracy. Empirical validation using large-scale real-world traffic data demonstrates that our model improves prediction accuracy by 26.2% over standard LSTM methods while reducing CPU usage by 75%. Moreover, the proposed framework supports a dual-driven decision paradigm by linking micro-level driver behavior with macro-level congestion evolution, offering interpretable insights for smart traffic control policies such as dynamic lane assignment and variable speed limits. This study contributes to the development of robust and scalable solutions for intelligent transportation systems and sustainable urban mobility.