基于双路信息时空图卷积网络的交通预测模型

Traffic Prediction Model Based on Dual Path Information Spatial-Temporal Graph Convolutional Network

随着深度学习的发展,神经网络在各个领域都有着大量的应用,智慧交通系统也不例外。交通流预测是智慧交通系统的基石,是整个交通预测的核心所在。近年来,图卷积神经网络的利用有效地提高了交通预测的性能,如何进一步提高对图的时空特征进行捕获的能力,将会成为热点。为了提升交通预测的精度,提出了一种基于双路信息时空图卷积网络的交通预测模型。首先,针对图卷积网络的交通预测模型在长距离依赖上建模有所不足,并且没有完全挖掘时空图信息之间的隐藏关系以及在时空图结构上还有信息缺失,提出了一种三重池化注意力机制来建模全局上下文信息。通过对图卷积层和时间卷积层各增加并行的三重池化注意力路径,构造了一个双路信息时空卷积层,提升了卷积层的泛化能力及模型捕获长距离依赖的能力,同时让时空卷积层能够很好地捕获时空图结构上的空间和时间特征,从而有效地提升了交通预测性能。在两个公共交通数据集(METR-LA和PEMS-BAY)上的实验结果表明,该模型具有较好的性能。

With the development of deep learning,neural network has a large number of applications in various fields,and intelligent transportation system is no exception.Traffic flow forecast is the cornerstone of intelligent traffic system and the core of the whole traffic forecast.In recent years,the use of the graph convolutional neural network has effectively improved the performance of traffic prediction.How to further improve the ability to capture the spatial and temporal characteristics of the graph will become a hot topic.In order to improve the accuracy of traffic prediction,this paper proposes a traffic prediction model based on the convolution network of dual path information spatial-temporal map.First of all,the traffic prediction model based on the graph convolution network has some shortcomings in long-distance dependence modeling,and has not fully mined the hidden relationship between the spatial-temporal diagram information and the missing information in the spatial-temporal diagram structure,so we propose a triple pooling attention mechanism to model the global context information.Based on the figure of each increase in parallel convolution layer and the time convolution triple pooling attention path,we construct a dual path information spatial-temporal convolution layer,enhance the generalization ability of convolution layer,improve the model’s ability to capture long distance dependence,and spatial-temporal convolution layer can capture figure characteristics of space and time structure of spacetime,effectively improve the traffic prediction performance.Experimental results on two public transport data sets(METR-LA and PEMS-BAY)show that the proposed model has good performance.