ST-ParFusion: Spatio-Temporal Parallel Fusion Network for Traffic Flow Prediction Based on Multi-Relation Driving

Accurate and real-time traffic state prediction is fundamental to optimizing urban traffic control and improving the service quality of online mapping platforms. While data-driven deep learning methods have shown promise in handling large-scale data, most existing approaches suffer from limitations due to their reliance on simplistic modeling components that cannot fully capture complex and multi-scale spatiotemporal dependencies. Unlike prior studies that predominantly utilize static graph structures or sequential processing, this research addresses the challenge of modeling dynamic correlations by proposing a Spatiotemporal Parallel Fusion Network. The theoretical framework of this study integrates graph spectral theory with advanced attention mechanisms to model both the physical and semantic topology of traffic networks. The proposed methodology features a multi-source spatial modeling framework that fuses a static connectivity graph, an inverse distance weighted graph, and a flow similarity graph via multi-relational graph convolution. This approach allows for the simultaneous capture of physical connectivity and dynamic traffic patterns. Furthermore, utilizing a parallel architecture, the temporal modeling component integrates multi-head self-attention with two-dimensional causal dilated convolutions. A cross-attention fusion module is further introduced to dynamically regulate spatiotemporal interactions. Comprehensive tests conducted on four practical traffic datasets indicate that the introduced model significantly outperforms thirteen cutting-edge benchmark methods. The study confirms that the proposed multi-graph strategies and parallel architectures effectively reduce mean absolute error and root mean square error, validating the efficacy of the proposed solution for the advancement of intelligent transportation systems.