| Abstract |
The main objective of this study is to explore and evaluate the application potential of combining Sixth Generation (6G) technology and random forest (RF) algorithm in the intelligent traffic safety system. This study designs and implements an intelligent traffic safety system using the RF algorithm and 6G technology, to improve the traffic conditions' real-time monitoring and prediction ability. The advantages of 6G technology in real-time data transmission and efficient data processing. Moreover, the application of the RF algorithm in traffic congestion and accident prediction is discussed. The value of these techniques in improving prediction accuracy, system stability, and safety performance is analyzed. Extensive experimental tests are carried out in multiple traffic scenarios by constructing modules such as data collection and preprocessing, model training and optimization, real-time data processing, and system integration and display. In the experimental test, two main scenarios of traffic congestion warning and accident prediction are designed. The results reveal that in the traffic congestion warning scenario, under the condition that the traffic flow is 1800-2000 vehicles/hour and the average speed is 45-55 km/h, the prediction accuracy reaches 96%, the recall is 99%, and the F1 score is 97%. In the traffic accident prediction scenario, the system's prediction accuracy, recall, and F1 score are 92%, 95%, and 93% when the traffic flow is 1200-1400 vehicles/hour on rainy days. The results of this study provide practical technical solutions for smart city traffic management and explore the prospect of future intelligent transportation system development, thus offering a theoretical and empirical basis for research and practice in related fields. © 2000-2011 IEEE. |
