Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. Accurate short-term traffic flow prediction also provides necessary conditions for the sustainable development of the traffic environment. Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established. In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. The CDLP model is trained and tested, and the prediction results of traffic flow are obtained and analyzed. It indicates that the CDLP model can fit the change trend of traffic flow very well and has better performance. Furthermore, under the same dataset, the results from the CDLP model are compared with baseline models. It is found that the CDLP model has higher prediction accuracy than baseline models.

中文翻译：

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短期交通流量预测：一种结合深度学习的方法

短期交通流预测可以为交通管理提供依据，为出行者决策提供支持。准确的短期交通流预测也为交通环境的可持续发展提供了必要条件。虽然深度学习方法在交通流预测中的应用已经取得了较好的准确率，但是结合多种深度学习方法来提高单一方法的预测准确率的问题仍有深入研究的余地。在本文中，建立了一个组合深度学习预测（CDLP）模型，包括两个并行的单一深度学习模型，CNN-LSTM-attention 模型和 CNN-GRU-attention 模型。在模型中，使用一维卷积神经网络 (1DCNN) 提取交通流局部趋势特征，使用具有注意力机制的 RNN 变体（LSTM 和 GRU）提取长时间依赖性趋势特征。此外，提出了一种动态最优加权系数算法（DOWCA）来计算CNN-LSTM-attention和CNN-GRU-attention的动态权重，目标是最小化CDLP模型的误差平方和。然后，讨论并通过实验设置CDLP模型的神经元数、损失函数、优化算法等参数。最后，通过对现场采集的交通流数据进行处理，建立CDLP模型的训练集和测试集。对CDLP模型进行训练和测试，得到交通流的预测结果并进行分析。表明CDLP模型能够很好地拟合交通流量的变化趋势，具有更好的性能。此外，在相同的数据集下，将 CDLP 模型的结果与基线模型进行比较。发现CDLP模型比基线模型具有更高的预测精度。