An enhanced Spatiotemporal Network (STN) integrating Scalar LSTM (sLSTM), a more efficient variant designed to improve temporal modeling while reducing computational complexity, is proposed, demonstrating superior generalization for 5G traffic prediction.
The research method provides a scalable, high-precision, end-to-end solution for cloud-edge-integrated traffic prediction and collaborative management and control for real-time, accurate traffic prediction in urban congestion.
Hai-Long Dong· Journal of Engineering, Proj...· 0 citations
A node-tokenized GPT-2 framework is proposed and attention adaptation and residual prediction mechanisms for traffic flow forecasting are investigated, indicating that residual prediction improves forecasting accuracy, whereas simply modifying the attention structure does not necessarily lead to reliable modeling of tr...
Ming-Zhu Gao, Ruo-Han Ning· International Conference on...· 0 citations
The model incorporates three core components: spatiotemporal embedding module, spatiotemporal fusion module, and LLM backbone, which adopts a differentiated parameter adaptation strategy to balance training efficiency and traffic data adaptability.
SASTFormer is a method for traffic flow prediction that relies on fusing spatiotemporal multi-head self-attention to enhance long-term prediction and outperforms eight baseline models in overall performance and medium-/long-term prediction on PeMS08, but also delivers more stable predictive accuracy.
Xun-Qiang Gong, Sheng Luo, Qi Liang et al.· International Conference on...· 0 citations
With the evolution of modern intelligent networks, accurate traffic matrix (TM) prediction has become critical for proactive network management, but remains challenging due to high-dimensional and non-stationary spatio-temporal dependencies. We propose TraDiff, an image-based conditional diffusion framework for TM pred...
Yu Sun, Ya-Qiong Liu, Nan Cheng et al.· IEEE International Symposium...· 0 citations
ASTPKEformer is proposed, a prior knowledge-guided Trans-former framework for traffic prediction that outperforms state-of-the-art (SOTA) baselines, validating its effectiveness and enhancing the representation capability.
Wenfeng Zhou, Xiao-Yun Xia, Xiang-Jie Kong et al.· Proceedings of the Thirty-Fi...· 0 citations
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