Prediction And Analysis Of Road Traffic Efficiency Based On DBN-SVR
F. Li-Zeyu, S. Ge-Xiaoyu · 2019
With the development of China's economy, the number of private cars has increased significantly, leading to heavy traffic pressure on the road network and a serious decline in traffic efficiency, which has become a shackle hindering urban development. This paper takes traffic data as the research object, establishes a DBN network model with a top-level predictor, and extracts three influencing factors affecting traffic flow, namely lane number, time ratio of red and green signal lights, and whether to turn left, through learning the past traffic data. On this basis, SVR algorithm is combined with the above traffic impact factors to make short-term prediction of the future traffic flow of the road section. Finally, the prediction results are used to optimize the road efficiency to improve the road network efficiency. In this paper, a simulation experiment is carried out on the real road network data of jinan city, and the experimental analysis proves that the method proposed in this paper can effectively improve traffic congestion and improve traffic efficiency. This method has important theoretical significance and practical value for intelligent road construction.