Li Xuan, He Muyang, Qin Dong, Zhou Tianqing, Jiang Nan
School of Information and Software Engineering, East China Jiaotong University, Nanchang 330013, China.
School of Information Engineering, Nanchang University, Nanchang 330031, China.
Sensors (Basel). 2025 Aug 18;25(16):5127. doi: 10.3390/s25165127.
In the realm of urban vehicular ad hoc networks (VANETs), cross-domain data has constituted a multifaceted amalgamation of information sources, which significantly enhances the accuracy and response speed of traffic prediction. However, the interplay between spatial and temporal heterogeneity will complicate the complexity of geographical locations or physical connections in the data normalization. Besides, the traffic pattern differences incurred by dynamic external factors also bring cumulative and sensitive impacts during the construction of the prediction model. In this work, we propose the spatio-temporal heterogeneity-oriented graph convolutional network (SHGCN) to tackle the above challenges. First, the SHGCN analytically employs spatial heterogeneity between urban streets rather than simple adjacency relationships to reveal the spatio-temporal correlations of traffic stream movement. Then, the air quality data is taken as external factors to identify the traffic forecasting trend at the street level. The hybrid model of the graph convolutional network (GCN) and gated recurrent unit (GRU) is designed to investigate cross-correlation characteristics. Finally, with the real-world urban datasets, experimental results demonstrate that the SHGCN achieves improvements, with the RMSE and MAE reductions ranging from 2.91% to 41.26% compared to baseline models. Ablation studies confirm that integrating air quality factors with traffic patterns enhances prediction performance at varying degrees, validating the method's effectiveness in capturing the complex correlations among air pollutants, traffic flow dynamics, and road network topology.
在城市车辆自组织网络（VANETs）领域，跨域数据构成了一个多方面的信息源融合体，这显著提高了交通预测的准确性和响应速度。然而，空间和时间异质性之间的相互作用会使数据归一化中地理位置或物理连接的复杂性变得更加复杂。此外，动态外部因素引起的交通模式差异在预测模型构建过程中也会带来累积和敏感的影响。在这项工作中，我们提出了面向时空异质性的图卷积网络（SHGCN）来应对上述挑战。首先，SHGCN分析性地利用城市街道之间的空间异质性而非简单的邻接关系来揭示交通流移动的时空相关性。然后，将空气质量数据作为外部因素来确定街道层面的交通预测趋势。设计了图卷积网络（GCN）和门控循环单元（GRU）的混合模型来研究互相关特性。最后，通过真实世界的城市数据集，实验结果表明，与基线模型相比，SHGCN实现了改进，RMSE和MAE的降低幅度在2.91%至41.26%之间。消融研究证实，将空气质量因素与交通模式相结合在不同程度上提高了预测性能，验证了该方法在捕捉空气污染物、交通流动态和道路网络拓扑之间复杂相关性方面的有效性。
