Application And Research Of Traffic Accident Prediction With PSO-BPNN

With economic development, all kinds of motor vehicles have become a very important way to travel. However, the frequent occurrence of road traffic accidents has continued to threaten all people's lives and property. Therefore, the Department of Transportation authorities hope the long term accumulation of historical data of traffic accidents, to extract valuable information, as relates to the transport safety related work to provide scientific decision support. Neural network as a data mining approach has long been widely used in various fields of data analysis and forecasting. PSO is a relatively new method in recent years, the particle swarm algorithm is applied to the BP neural network as its training algorithm, can optimize network performance, to overcome the limitations of BP algorithm. This paper studies the need for road traffic forecast to start, that projections in the traffic and the complexity of the problems faced; followed by the common accidents at home and abroad discussed the prediction method, and the necessary analysis of these methods, that their characteristics of the existing shortcomings; and BP neural network with this kind of feedback before the neural network model in solving the problems of complex nonlinear systems have the advantage of extensive training to learn the characteristics, as a road Accident prediction model will be made to different characteristics. This feature for the neural network is proposed by using BP neural network prediction method for road traffic accidents, and the establishment of forecasting model, the basic BP neural network model has a slow convergence and easy to fall into the minimum of defects, which PSO algorithm as the network training algorithms. In order to improve the neural network training and testing the reliability and relevance of the data, pretreatment of the sample data, using multiple regression analysis method with the combination to determine the neural network input variables, input variables and the soon to improve the relevance of predictor variables. Finally, using the standard BP neural networks and swarm optimization algorithm based on BP neural networks for modeling and testing, the experimental results were compared. Experimental results show that particle swarm optimization algorithm based on BP neural network model with higher accuracy.