Abstract: With the acceleration of urbanization,the number of motor vehicles in cities in China is increasing rapidly,which makes the existing road network capacity difficult to meet the transportation needs,traffic congestion,environmental pollution and traffic accidents are increasing day by day.Accurate and efficient traffic flow prediction,as the core of ITS,can effectively solve the problems of traffic travel and management.The existing short-term traffic flow prediction researches mainly use the shallow mo-del method,so they cannot fully reflect the traffic flow characteristics.Therefore,this paper proposed a short-term traffic flow prediction method based on DCGRU-RF model for complex traffic network structure.The DCGRU network is used to characte-rize the spatio-temporal correlation features in the traffic flow time series data.After obtaining the dependencies and potential features in the data,the RF model is selected as the predictor,and the nonlinear prediction model is constructed based on the extracted features,and finally getting the prediction result.In this experiment,38 detectors in two urban roads were selected as experimental objects,traffic flow data of five working days were selected,and the proposed model was compared with other common traffic flow prediction models.The experimental results show that DCGRU-RF model can further improve the prediction accuracy,the accuracy can reach 95%.

Key words: Combined forecasting model, Deep learning, Road network, Traffic flow forecast