基于CNN-ResNet-LSTM模型的城市短时交通流量预测算法

Urban Short-Term Traffic Flow Prediction Algorithm Based on CNN-ResNet-LSTM Model

针对交通流量特性和外部因素对交通流量预测结果的影响,提出了一种对城市短时交通流量预测的模型CNN-ResNet-LSTM,将卷积神经网络(CNN)、残差神经单元(Res Net)和长短期记忆循环神经网络(LSTM)集成到一个端到端的网络框架.利用卷积神经网络来捕获城市区域间交通流量的局部空间特征,并在卷积神经网络中加入多个残差神经单元来加深网络深度,可提高预测的准确性;利用长短期记忆循环神经网络来捕获交通流量数据的时间特征;利用相应的权重将2个网络的输出结果融合,得到通过轨迹数据预测的结果;最后与外部因素融合,得到城市区域的交通流量预测值.用北京市轨迹交通数据对该模型进行验证,CNN-ResNet-LSTM模型不仅在准确率方面比传统模型高,而且在保证预测准确率的情况下,模型使用的参数也少.

With the continuous advancement of smart city construction,urban short-term traffic flow forecasting becomes more and more important.According to the influence of traffic flow characteristics and external factors on traffic flow forecast results,the model CNN-ResNet-LSTM for urban short-term traffic flow forecasting is proposed.The model integrates convolutional neural networks(CNN),residual neural units(ResNet)and long-short-term memory networks(LSTM)into an end-to-end network framework.The convolutional neural network is used to capture the local spatial characteristics of traffic flow,and multiple residual neural units are added to deepen the network depth and improve the prediction accuracy.On the other hand,the long short-term memory-cycle neural network is used to capture temporal characteristics of traffic flow data.The output results of the two networks are combined by the corresponding weights to obtain the predicted results through the trajectory data.Finally,the traffic flow prediction values of the urban areas are obtained by fusing with external factors.Through the verification of the CNN-ResNet-LSTM model by Beijing data,the model is not only higher in accuracy than the traditional model,but also has fewer parameters in the case of ensuring the accuracy of prediction.