Traffic forecasting is a core element of intelligent traffic monitoring system. Approaches based on graph neural networks have been widely used in this task to effectively capture spatial and temporal dependencies of road networks. However, these approaches can not effectively define the complicated network topology. Besides, their cascade network structures have limitations in transmitting distinct features in the time and space dimensions. In this paper, we propose a Multi-adaptive Spatiotemporal-flow Graph Neural Network (MAF-GNN) for traffic speed forecasting. MAF-GNN introduces an effective Multi-adaptive Adjacency Matrices Mechanism to capture multiple latent spatial dependencies between traffic nodes. Additionally, we propose Spatiotemporal-flow Modules aiming to further enhance feature propagation in both time and space dimensions. MAF-GNN achieves better performance than other models on two real-world datasets of public traffic network, METR-LA and PeMS-Bay, demonstrating the effectiveness of the proposed approach.
翻译:交通流量预测是智能交通监测系统的核心要素。基于图形神经网络的方法已广泛用于这一任务,以有效捕捉公路网络的空间和时间依赖性。然而,这些方法无法有效地界定复杂的网络地形。此外,其级联网络结构在传输时间和空间方面的独特特征方面有局限性。在本文件中,我们提议建立一个多调的超时流图神经网络(MAF-GNN)进行交通速度预测。MAF-GNN引入一个有效的多调和相匹配矩阵机制,以捕捉交通节点之间的多种潜在空间依赖性。此外,我们提议建立时流模块,以进一步加强时间和空间两方面的特征传播。MAF-GNN在公共交通网络、METR-LA和PEMS-Bay两个真实世界数据集上取得比其他模型更好的业绩,展示了拟议方法的有效性。