Road traffic prediction plays a vital role in real-time traffic management of an intelligent transportation system (ITS). Many prediction models achieve fine results. However, most ignore the intrinsic characteristics of traffic parameter data and do not consider the spatiotemporal effects of road sections, which can reflect the situation of all road traffic. Therefore, multi-section traffic prediction is still an open problem. In this paper, empirical mode decomposition (EMD) is employed to decompose the information of traffic parameters into many intrinsic mode function (IMF) components, which represent the original road traffic information in periodic and random sequences. Then, by considering the superiority of deep learning in multi-dimensional data processing, which can handle the spatiotemporal effects, a prediction model based on a convolutional neural network (CNN) is proposed to achieve the prediction of periodic and random sequences, whose results are combined to obtain the final prediction. The dataset from the Caltrans Performance Measurement System is used to validate the model. The proposed prediction model is compared to several well-known models, such as PCA-BP, Lasso-BP, and standard CNN. Experiments show that the proposed prediction model achieves higher accuracy.

中文翻译：

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基于模式分解的多路段交通流量预测深度学习模型

道路交通预测在智能交通系统（ITS）的实时交通管理中起着至关重要的作用。许多预测模型均能获得良好的结果。但是，大多数人忽略了交通参数数据的内在特征，并且没有考虑路段的时空效应，因为它可以反映所有道路交通的状况。因此，多路段交通预测仍然是一个未解决的问题。本文采用经验模态分解（EMD）方法将交通参数信息分解为许多固有模式函数（IMF）组件，这些组件以周期性和随机序列表示原始道路交通信息。然后，考虑到深度学习在多维数据处理中的优势，该优势可以处理时空效应，提出了一种基于卷积神经网络（CNN）的预测模型，以实现对周期序列和随机序列的预测，并结合其结果以获得最终预测。来自Caltrans绩效评估系统的数据集用于验证模型。将拟议的预测模型与几个众所周知的模型进行比较，例如PCA-BP，Lasso-BP和标准CNN。实验表明，所提出的预测模型具有较高的准确性。