基于卷积神经网络与门控循环单元的交通流预测模型

为对交通流进行多步预测，支持智能交通系统的长期决策任务，一种基于编码器-解码器（encoder-decoder,ED）的卷积神经网络(convolutional neural networks,CNN)-门循环单元(gate recurrent unit, GRU)模型，简称ED CNN-GRU。首先使用CNN作为编码器，对交通流序列进行信息捕捉，再将上述信息通过GRU解码器进行解释并输出。实验证明，对比CNN、GRU单个模型，ED框架有效解决了误差的迅速累积问题。对比其他基准模型，CNN、 GRU模型对于交通流序列的特征提取及解释能力较为优秀。对于未来12个步长的交通流量预测任务，对比其他基准模型，单因素输入情况的ED CNN-GRU模型的均方根误差下降约0.344～6.464，平均绝对误差下降约0.192～0.425。对比单因素输入，多因素输入下ED CNN-GRU模型拥有更好的预测能力。证明了ED CNN-GRU模型在不同输入维度的多步交通流预测中任务中均具有良好的预测能力，为数据获取条件不同的城市提供了一个支持单因素及多因素输入情况的多步交通流预测模型。

Multivariable traffic flow prediction model based on convolutional neural network and gate recurrent unit

For multi-step forecasting of traffic flow, a convolutional neural networks (CNN)-gate recurrent unit (GRU) model based on encoder-decoder (ED) framework was proposed, referred to as the ED CNN-GRU model. In this model, CNN serves as the encoder, capturing information from the traffic flow sequence, which is then interpreted and outputted by the GRU decoder. Experimental results show that compared with CNN and GRU models, ED framework effectively solves the problem of rapid error accumulation. Compared with other benchmark models, CNN and GRU models are superior in feature extraction and interpretation of traffic flow series. In terms of the traffic flow prediction task of 12 steps in the future, compared with other benchmark models, the root mean square error of the univariate input ED CNN-GRU model is reduced by about 0.344 to 6.464, and the mean absolute error is reduced by about 0.192 to 0.425. Additionally, compared with univariate input, the ED CNN-GRU model with multivariate input exhibits a better fitting performance. These findings confirm that ED CNN-GRU model possesses strong forecasting capabilities for multi-step traffic flow forecasting tasks with varying input dimensions, and provides a multi-step traffic flow forecasting model that supports both univariate and multivariate input for cities with diverse data acquisition conditions.