Research On Traffic Flow Analysis Technology Based On Convolutional Networ

As the number of cars increases gradually,the construction of intelligent transportation systems has attracted the attention of governments and enterprises around the world.Intelligent transportation systems can use various sensors,communication technologies and data analysis to achieve real-time monitoring,prediction,and control of traffic conditions.Traffic flow prediction is an important component of intelligent transportation systems,which can predict the traffic demand and supply in a future period based on historical data and current information and provide decision support for traffic management.Traffic flow prediction is of great significance for alleviating traffic congestion,improving travel efficiency,planning urban development,etc.,and is also one of the core competencies of intelligent transportation systems.However,traffic flow prediction problem has high complexity and uncertainty,which not only needs to consider the topological structure of traffic network and the relationship between nodes,but also needs to consider the dynamic,periodic,nonlinear,and other characteristics of traffic data,as well as various factors that affect traffic flow,such as weather,holidays,accidents,etc.Therefore,traditional time series prediction models,such as autoregressive model,moving average model,etc.,cannot accurately model traffic flow data,nor can they capture the complex variation patterns of traffic flow.To address this challenge,this paper proposes two deep learning-based traffic prediction models for road node traffic flow prediction.The main research contents and contributions of this paper are as follows:The first proposed model is GC-ResNet,which is a hybrid model based on attention mechanism,graph convolutional network,and deep residual contraction units.It is designed for road nodes with strong spatial features and can capture both the spatial features of the transportation network and the temporal features of traffic flow information.The second proposed model is CU-ResNet,which is a hybrid model based on cross unit and deep residual contraction units.It is designed for road sections with less prominent spatial features and has excellent prediction performance while also having high generalization ability.The two models proposed in this paper are built based on Pytorch framework and modelled and predicted based on real traffic flow data.The experimental results of the two models show that both methods outperform traditional traffic prediction methods on different datasets and prediction time scales,proving that the models proposed in this paper have good accuracy and interpretability.This paper provides new ideas for the field of traffic prediction and valuable references for traffic management and planning.