文献类型：期刊文献

英文题名：Real-Time Traffic Flow Prediction for 6G Enabled Intelligent Transportation System

作者：Liu, Xin[1,2];Zhao, Haihang[1];Li, Jie[1,2];Han, Qi[1];Wu, Jingyuan[1]

机构：[1]Hebei Univ Technol, Sch Econ & Management, Tianjin 300401, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：26

期号：10

起止页码：18034

外文期刊名：IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

收录：;EI(收录号：20252318562839);WOS:【SCI-EXPANDED(收录号:WOS:001504170200001)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFB4503000, in part by the National Natural Science Foundation of China (NSFC) under Grant 62473129, in part by the Fundamental Research Funds for the Central Universities under Grant 2024SMECP04, in part by the Natural Science Fund of Hebei Province for Distinguished Young Scholars under Grant F2021202010, and in part by the Science and Technology Project of Hebei Education Department under Grant JZX2023007.

语种：英文

外文关键词：Predictive models; Optimization; Neural networks; Prediction algorithms; Forecasting; Biological system modeling; Accuracy; 6G mobile communication; Real-time systems; Noise; Intelligent transportation system; traffic flow prediction; deep fuzzy rough neural network; large-scale multiobjective optimization algorithm; evolutionary algorithm

摘要：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.