Research And Design Of Intelligent Road Network Command System Based On Video Traffic

With the rapid urbanization process,the number of cars has increased rapidly,which has caused the problem of urban road traffic congestion to become more and more serious.In order to effectively alleviate the problem of urban road congestion,intelligent transportation has become an important part of smart city construction and the current research hotspot.In 2019,traffic signal control has become one of the most concerned people's livelihood in the field of intelligent transportation.Under the original road construction conditions,the question of how to alleviate urban traffic congestion,improve road network traffic efficiency and management capabilities,and solve the current concerns about travel has important theoretical significance and research value.In this thesis,the traffic signal optimization of urban road intersections is studied,and the traffic flow of the current road is obtained in real time by using the video captured by traffic road monitoring.The current traffic flow data is input into the error correction wavelet neural network prediction model,and the traffic flow of the next period of the intersection is predicted.The traffic flow of the current intersection and regional road network is adjusted in real time according to the prediction value of the model.The signal control strategy improves the efficiency of road traffic and the level of traffic management.On this basis,the intelligent road network command system based on video traffic flow is designed.In this thesis,in terms of algorithm research,first of all,in order to obtain more accurate traffic flow information in the video,a dynamic target detection algorithm combining improved ViBe and adaptive threshold shadow elimination algorithm is proposed.The algorithm uses mean background modeling to perform the ViBe algorithm Improvement,solved the ghost problem ofelimination algorithm,effectively eliminated the shadow in the foreground image,made the target detection more accurate,and statistics for the subsequent traffic data lay the foundation.Secondly,in order to improve the accuracy of the wavelet neural network prediction model,error correction is performed on it,the time series prediction model is used to predict the error.The predicted value of the error is used to modify the prediction result of the wavelet neural network prediction model to improve the accuracy of the prediction.Finally,combined with the multi-objective optimization model,the timing strategy of the intersection is optimized and adjusted to achieve real-time control and intelligent command of the intersection.The thesis has carried out experiments and simulations for each part of the research.The algorithms and models proposed in this paper are stable and achieve good results.At the end of the thesis,the overall design,software platform design,database design and interface design process of the intelligent road network command system are given,and the system software platform is tested.The results show that the system is stable and reliable,and meets the design requirements.