Researh Of Freeway Traffic Incident Detection Based On Adaboost And Levenberg Marquard Back Propagation Neural Network

The construction and development of the freeway has brought much convenience and efficiency to people, however, at the same time we need pay attention to the security issues caused due to it. Traffic congestion, traffic accidents and other types of traffic incidents occur on the freeway, not only reduce the transport efficiency and operational effectiveness of the freeway, and also cause plenty of serious losses of people’s lives and properties. How to quickly detect and deal with traffic incidents, effectively reduce the traffic incident hazards has become a highly topical issue. The detection algorithm is the core of the traffic incident detection technology which directly impact on the performance of traffic incident detection. Therefore, the research of the detection algorithm is of great significance.Most of the detection algorithms find the corresponding relationship of traffic incidents and traffic flow parameters by analyzing traffic flow data. Therefore, in this paper we firstly analyze the traffic incidents and traffic flow parameters’characteristics, then study the BP neural network and its learning algorithm. Finally we choose the LM algorithm in my paper after analysis and comparison of various BP algorithms and use the N-W dispersion select method to optimize LMBP neural network’s initial weights. Based on those, we design traffic incident detection algorithm due to the improved LMBP neural network, and make detailed designation of LMBP network structure and parameters in the model building process, then use the built algorithm model to detect traffic incident. At last, according to the principle of "the more the difference of weak classifiers, the better the integration result", we use different combinations of traffic flow parameters to constitute LMBP weak classifiers in different network structures, and put in use of the AdaBoost method to integrate improved LMBP model, and build model to detect traffic incident.This paper use1-880data in simulation, the result show that the use of this N-W dispersion select method optimization LMBP’s initial weights greatly raise the convergence speed of the detection algorithm, further improve the detection precision in combination with the AdaBoost method and obtain a good detection performance in the end.