Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation, but due to the complex spatiotemporal characteristics of traffic flow, its forecasting has always been difficult. Deep learning can learn the deep spatiotemporal characteristics of traffic flow from a large amount of data. Deep learning can learn the deep spatiotemporal characteristics of traffic flow from a large amount of data. This paper establishes a novel combination forecasting model GGCN-SA based on deep learning for traffic flow to effectively capture the spatiotemporal characteristics of traffic flow and improve forecasting accuracy. The model captures the spatial correlation of the road traffic network through the graph convolutional network (GCN), captures the time dependence of the traffic flow through the gated recursive unit (GRU), and further introduces the soft attention mechanism (Soft Attention) to aggregate different neighborhoods Spatio-temporal information within the range to enhance the model’s ability to characterize the temporal and spatial characteristics of traffic flow. A large number of experiments have been conducted on the METR-LA and SZ-taxi data sets. The experimental results show that the GGCN-SA model proposed in this paper has better forecasting performance compared with the baseline methods.

中文翻译：

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基于图卷积网络和注意力机制的组合交通流预测模型

准确的交通流预测是实现智能交通的前提保障，但由于交通流的复杂时空特征，其预测一直比较困难。深度学习可以从大量数据中学习交通流的深层时空特征。深度学习可以从大量数据中学习交通流的深层时空特征。本文建立了一种基于深度学习的交通流组合预测模型GGCN-SA，以有效捕捉交通流的时空特征，提高预测精度。该模型通过图卷积网络（GCN）捕捉道路交通网络的空间相关性，通过门控递归单元（GRU）捕获交通流的时间依赖性，并进一步引入软注意力机制（Soft Attention）聚合不同邻域范围内的时空信息，增强模型对时空的刻画能力交通流特征。在 METR-LA 和 SZ-taxi 数据集上进行了大量的实验。实验结果表明，与基线方法相比，本文提出的GGCN-SA模型具有更好的预测性能。在 METR-LA 和 SZ-taxi 数据集上进行了大量的实验。实验结果表明，与基线方法相比，本文提出的GGCN-SA模型具有更好的预测性能。在 METR-LA 和 SZ-taxi 数据集上进行了大量的实验。实验结果表明，与基线方法相比，本文提出的GGCN-SA模型具有更好的预测性能。