Refined Judgment of Urban Traffic State Based on Machine Learning and Edge Computing - Lijuan Liu (Journal of Advanced Transportation)
Machine learning is a discipline that covers probability theory, statistics, approximate theoretical knowledge, and complex algorithm knowledge. It is committed to real-time simulation of human learning methods, which can effectively improve learning efficiency. The main function of this calculation method is to use a relatively open platform to integrate the Internet, computers, memory, and other terminal applications through integrated technical means. For providing short-distance services and applications, we should start from the edge of the program to create a faster network. Service response covers the basic needs of real-time processing industry, intelligent applications, and security and privacy protection. This paper aims to study the recognition of urban traffic conditions and refine the recognition through the improvement of edge computing algorithms. This paper proposes a method to calculate traffic flow parameters, preprocess the traffic flow data, delete irrelevant features, use flow theory to delete wrong data, and change the data in time according to actual needs, carrying out refined discrimination and analysis of urban traffic status. The experimental results in this paper show that the use of edge computing to fine-tune the state of urban traffic can divert traffic, greatly reduce traffic congestion, and increase traffic safety by 13%. Among them, the efficiency of road network time and space resources has also increased by 23%.