想用Transformer预测城市网络流量？这个PEHT用LoRA大幅减少参数，还能融合拥堵数据，实测精度超过现有方法。
论文提出参数高效混合Transformer（PEHT），将LoRA集成到Transformer编码器中以减少可训练参数，同时在解码器中融合外部移动性和拥堵特征。在Telecom Italia Milan数据集和多个合成拥堵场景上的实验显示，PEHT在RMSE、MAE和R²指标上优于现有基线。该模型针对动态城市蜂窝网络中的资源分配优化，代码已在GitHub开源。
Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks. However, prediction remains challenging because network demand is influenced by complex mobility patterns, congestion dynamics, and heterogeneous user behavior. This paper introduces the Parameter-Efficient Hybrid Transformer (PEHT), a network traffic prediction framework that integrates urban mobility and congestion information into a Transformer-based architecture. PEHT separates primary network communication features from secondary urban mobility features and incorporates Low-Rank Adaptation (LoRA) into the Transformer encoder to reduce the number of trainable parameters while maintaining high predictive accuracy. A multimodal fusion strategy then injects external mobility and congestion features into the decoder to improve traffic forecasting. Experiments on the Telecom Italia Milan dataset and multiple synthetic congestion scenarios show that PEHT outperforms state-of-the-art baselines in terms of RMSE, MAE, and $R^2$. The implementation is available in the GitHub repository.