Expressway traffic safety early warning system based on cloud architecture

Abstract

With the development of the society, highway traffic safety is gradually valued by the world. However, due to the complicated state of the highway roads, the faster road speeds and the different types of vehicles, the problem of highway safety warning is an extremely complex system engineering problem faced by the entire society. In view of the characteristics of the problem studied, this study firstly conducted a simple analysis of the design goals and overall architecture of the highway traffic safety early warning system; on this basis, the various components of the system-road information collection, road information processing analysis and road the early warning information release and other functional modules have been elaborated and analyzed accordingly. The road information collection and cloud architecture are combined to solve the problem of excessive data generation. Finally, the important link of analysis and early warning-highway status classification Problem, the BP neural network algorithm is proposed. Through the BP neural network algorithm, the road nodes are classified, and then the safety warnings are generated according to the road status information. The safety warnings are divided into four levels: the first level is particularly dangerous and vehicle traffic is strictly prohibited; the second level is more dangerous and requires vehicles to bypass; the third level is a certain danger, the vehicle is required to pay attention to the prompt information, and you must go to the service area to rest for a long time Through the BP neural network algorithm, the efficiency of node classification is improved by 13%.