Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network

Abstract

The urbanization is the sign of advanced development for an urban. In recent years, with the development of science, technology and economy and the rise of urban car ownership, urban road traffic became a severe problem. There occurred a huge number of urban road traffic accidents frequently. To study and find insufficiency for the research status at home and abroad, the four aspects --man - vehicle - road - environment are analyzed, and the comprehensive analysis of the present safety situation of urban road intersection is made. Selecting one in seven important influencing factors of urban road intersection index as a Back Propagation (BP) neural network input, the early warning model, based on BP neural network, is established. Data of existing urban road intersections is analyzed, and the results show that the BP neural network can be well applied to early warning and forecast model analysis of urban road intersection accident, thus it facilitates for the traffic administrative department of the city road intersection to predict the accident frequency of urban road intersection for the traffic accident in the future, take appropriate intervention measures and improve the safety status of urban road intersection.