The Application of 6G Technology and Back Propagation Neural Network in the Smart Public Transport System

To clarify the applications of the sixth-generation (6G) mobile communication technology and neural networks in the smart public transport system, firstly, this system’s overall framework is constructed. 6G technology is integrated into this framework to enable real-time collection and updating of bus information data, supporting intelligent services, enhancing overall efficiency, service quality, and user experience of public transport. Secondly, a vehicle travel time prediction model based on Back Propagation Neural Network (BPNN) is established. Furthermore, addressing the shortcomings of BPNN, genetic algorithm (GA) is employed to optimize the connection weights and thresholds of BPNN, thereby improving its performance. Finally, a real-time scheduling scheme for smart public transport is designed based on 6G technology and BPNN, and the model and system performance are verified through experiments. The results indicate that the mean relative error, mean absolute error, and root mean square error between predicted and actual values of the model before optimization were 48.61%, 51.77, and 0.863 respectively. After optimization, these metrics improved significantly to 10.54%, 19.07, and 0.152, demonstrating a notable enhancement in prediction accuracy compared to before optimization. Meanwhile, user satisfaction with the smart public transport system gradually increased, from 75% in March to 92% in November. This indicates that user satisfaction with the proposed system has gradually improved over time. Therefore, the research results provide a theoretical basis and robust technical support for the smart public transport system’s construction and promote the intelligent and modern development of public transport services.