Intelligent urban traffic management system based on Internet of Things and deep learning
With the rapid development of urbanization,urban traffic problems are becoming increasingly severe.Accurate prediction of traffic flow is key to easing traffic congestion and improving traffic management efficiency.This paper proposes an urban traffic flow prediction method based on LSTM.This method first preprocesses traffic data,including data cleaning,normalization,etc.Then,the LSTM model is used to learn the spatiotemporal dependence of traffic data.Finally,the trained model is used to predict future traffic flow status.In order to verify the effectiveness of the method,experiments were conducted on the METR-LA data set,and the experimental results demonstrated the effectiveness of the method.This method can be applied in actual traffic management systems to provide decision support for traffic management departments,such as traffic light control,traffic route planning,etc.
traffic flow predictionLSTMtime series predictionsmart city
NETL
NSTL
万方数据
