The Intelligent Traffic Flow Control System Based on 6G and Optimized Genetic Algorithm

With the acceleration of urbanization, the problem of urban traffic congestion has become increasingly serious. This study aims to develop an intelligent traffic flow control system based on sixth-generation (6G) communication technology to achieve real-time prediction and management of traffic flow, optimize signal control, and reduce accident rates, thereby improving urban traffic efficiency and safety. This study constructs an intelligent traffic flow control system framework consisting of network, platform, perception, and application layers. The system utilizes vehicle networking technology and various sensors to collect traffic data and ensures real-time transmission of data through 6G communication technology. Meantime, the optimized genetic algorithm is used to optimize the traffic flow, solve the complex traffic optimization problems, and improve the overall efficiency of the traffic system. The platform layer employs machine learning algorithms to analyze and process data, predict traffic flow, and formulate traffic management strategies. The system's effectiveness is verified through experiments and comparative analysis with actual data. The results show that the error rate of traffic flow prediction during peak hours is as low as 0.81%, and signal control optimization reduces the average traffic delay by 15%. By implementing improved traffic management measures, the average annual accident rate has decreased by over 40%, and user satisfaction has increased by 30%. It can be found that the 6G-based intelligent traffic flow control system can effectively enhance the accuracy and response speed of traffic prediction, optimize signal control, remarkably reduce the accident rate, and improve the overall satisfaction of users.