Real-Time Traffic Flow Prediction for 6G Enabled Intelligent Transportation System

The sensing-computing integrated chips and systems can be used for intelligent transportation to process and acquire traffic data. Traffic data can be used to effectively forecast real-time traffic flow at a specific future time, which is crucial for promoting efficient transportation systems and supporting economic development in the era of 6G. However, traditional real-time traffic flow prediction models exhibit poor performance when dealing with noise, uncertainty, and nonlinear data. To address this issue, this paper constructs a deep fuzzy rough neural network model based on large-scale multiobjective optimization algorithm(LMO-DFRNN) for real-time traffic flow prediction. By simultaneously optimizing multiple objectives, the model achieves an optimal balance between performance and simplicity in traffic flow tasks. To improve the model’s accuracy and adaptability in real-time traffic flow forecasting, this study presents a large-scale multiobjective optimization method that uses a state-information-based dynamic balancing evaluation strategy. The evaluation method comprises diversity and convergence, each corresponding to a specific factor. By dynamically adjusting the weights of two factors based on the individual performance on diversity and convergence, a balance between these two indicators is achieved. The experiments were conducted by using real-world traffic flow datasets, and the findings reveal that, in comparison with five advanced models, the proposed model achieved reductions in the evaluation metrics MAE, RMSE and MAPE by 43.73%, 46.22%, and 34.87% respectively.