基于双路先验自适应图神经常微分方程的交通流预测

Traffic Flow Prediction Model Based on Dual Prior-adaptive Graph Neural ODE Network

准确的交通流量预测是智能交通系统不可或缺的组成部分。近年来,图神经网络在交通流预测任务中取得了较好的预测结果。然而,图神经网络的信息传递是不连续的潜在状态传播,且随着网络层数的增加存在过平滑的问题,这限制了模型捕获远距离节点的空间依赖关系的能力。同时,在表示道路网络的空间关系时,现有方法大多仅使用先验知识构建的预定义图或仅使用路网状况构建的自适应图,忽略了两类图结合的方式。针对上述问题,提出了一种基于双路先验自适应图神经常微分方程的交通流预测模型。利用时间卷积网络捕获序列的时间相关性,使用先验自适应图融合模块表示道路网络的空间关系,并通过基于张量乘法的神经常微分方程以连续的方式传播复杂的时空特征。最后,在美国加利福尼亚州4个公开的高速公路流量数据集上进行对比实验,结果表明所提模型的预测效果优于现有的10种对比方法。

Accurate traffic flow prediction is an indispensable part of intelligent transportation system.In recent years,graph neural networks have generated effective results in traffic flow prediction tasks.However,the information transfer of graph neural network is discontinuous latent state propagation,and there is an over-smoothing problem as the number of network layers increases,which limits the ability of the model to capture the spatial dependencies of distant nodes.At the same time,when representing the spatial relationship of the road network,most of the existing methods only use the predefined graph constructed by prior knowledge or the adaptive graph constructed only by the road network conditions,ignoring the combination of those two graphs.Aiming at solving the above problems,this paper proposes a traffic flow prediction model based on a dual prior adaptive graph neural ordinary differential equation.Temporal convolutional network are utilized to capture the temporal correlation of sequences,a priori adaptive graph fusion module is used to represent the road network,and complex spatio-temporal features are propagated in a continuous manner through tensor multiplication-based nerual ODEs.Finally,experiments are carried out on four public data sets of highway traffic in California,USA.Experimental results show that the prediction performance of the model is better than that of the existing ten methods.