Forecasting Model of Road Traffic Accident Based on Improved BP Neural Network
基于改进BP神经网络的道路交通事故预测

Due to the fact that it is affected by various random factors, a traffic accident is nonlinear in nature. Thus, its essence can not be efficiently revealed by traditional linear analysis method. Starting from the analysis of the relation between traffic accident and factors, including human, vehicle and road, and employing, the nonlinear characteristics described by a neural network of a road traffic accident, a forecasting model based on improved BP, is proposed by integrating the factors affecting traffic. A traffic accident prediction model uses population density,road network density, and motor vehicle density as the input neurons and an output neuron which is road accident comprehensive mortality. The results show that the improved BP neural network is well-suited for the forecasting of road traffic accidents, thus, verifing the feasibility and effectiveness of the model is verified.