基于卷积神经网络与双向长短时记忆网络组合模型的短时交通流预测

Short-term traffic flow prediction based on combined model of convolutional neural network and bidirectional long-term memory network

及时准确的交通流信息对于智能交通系统的实现至关重要。针对现有预测方法不能充分利用交通流的时空特征,进而不能很好地提取交通流序列内在规律的问题,提出了一种结合卷积神经网络(CNN)和双向长短时记忆网络(BiLSTM)的深度学习预测模型(C-BiLSTM),在网络底层利用一维CNN来捕获观测点交通流数据的空间特征,然后输入到双向LSTM网络提取时间周期特征,最后由全连接层输出预测结果。使用美国交通研究数据实验室的实测交通数据进行验证,结果表明,所提出的C-BiLSTM组合模型具有更高的准确性,其性能相比双向LSTM网络预测模型提升了1.6%,相比单向LSTM网络预测模型提升了6.6%,是一种高精度的交通流预测模型。

Timely and accurate traffic flow information is critical to the realization of intelligent transportation systems.In view of the fact that the existing prediction methods can not fully utilize the temporal and spatial characteristics of traffic flow,and can not extract the inherent law of traffic flow sequence well,a combination of convolutional neural network(CNN)and bidirectional long-term memory network(BiLSTM)is proposed.The deep learning prediction model(C-BiLSTM)has the advantage of using the one-dimensional CNN to capture the spatial characteristics of the observation point traffic flow data at the bottom of the network.The advantages are then input to the bidirectional LSTM network to extract the time period features,and finally the full connection layer outputs the prediction results.Validated traffic data from the US Transportation Research Data Laboratory was used for verification.The results show that the proposed C-BiLSTM combination model has higher accuracy,and its performance is improved by 1.6%compared with the bidirectional LSTM network prediction model.Compared with the LSTM network prediction model,it is increased by 6.6%.It is a high-precision traffic flow prediction model.