Abstract: The traffic flow sequence is not stationary, periodic and easily affected by factors such as holidays, thus it is hard to predict. Therefore, a model based on deep learning is designed. Model includes convolution neural network and LSTM（Long Short-Term Memory）. Convolution neural network is used to extract feature components. LSTM uses the extracted features to sequence prediction. The extracted data are used as input of LSTM, and output as a predictive results. This model has better accuracy, real-time and generalization performance, which is testified through the data of freeway traffic in Guizhou province.

Key words: traffic flow forecasting, time series analysis, Convolution Neural Network（CNN）, Long Short-Term Memory（LSTM）, feature extraction

摘要： 交通流量序列具有不平稳性、周期性、易受节假日等因素影响的特点，因此交通流量预测是一项困难的任务。针对交通流量序列的预测问题，设计了一种基于深度学习的交通流量预测模型。模型融合了卷积神经网络和长短时记忆神经网络两种网络结构，卷积神经网络用于提取特征分量，长短时记忆神经网络综合提取出来的特征分量做序列预测。通过在贵州省高速公路车流量数据集上的验证，模型比传统的预测方法具有更高的精确度和实时性，在不同数据集上的泛化性能良好。

关键词: 交通流量预测, 时间序列分析, 卷积神经网络, 长短时记忆神经网络, 特征提取