Prediction of Traffic Congestion Level on Urban Main Roads Based on Deep Learning
In order to solve the problem of traffic congestion on urban main roads,a deep learning based method for predicting the level of traffic congestion on urban main roads is proposed.A Katz similarity matrix is established for the traffic network of urban main roads.It can preserve the structural features of the road network and obtain traffic flow data of urban main roads.The local sensitivity discriminant analysis model is used to map the traffic flow data to the low dimensional manifold,obtain the optimal projection matrix,and extract the traffic characteristics of urban main roads.Combining the recurrent neural network model(RNN)and the long-term and short-term memory network model(LSTM),the recurrent neural network model for long short-term memory(RNN-LSTM)model is designed to solve the vanishing gradient problem.The network is inputted the traf-fic characteristics of urban main roads,and outputs the prediction results of traffic congestion level of urban main roads after training.The experimental results show that the prediction accuracy of proposed method is between 0.8～0.98,and the aver-age prediction time is 24.74 ms,which has certain application value.
deep learningurban main roadsrecurrent neural network model for long short-term memoryKatz similarity ma-trixprediction of traffic congestion level
万方数据
维普
