用低参数量融合城市拥堵数据，提升移动网络流量预测精度
Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
- 分离通信与城市移动特征，用低秩适配降低参数量
- 在米兰电信数据集上RMSE降低12.3%，R²达0.94以上
- 适合交通与网络协同优化研究者参考
精准的网络流量预测对动态城市蜂窝网络中的资源高效分配至关重要。然而，由于网络需求受复杂移动模式、拥堵动态和异构用户行为影响，预测仍具挑战性。本文提出参数高效的混合注意力模型（PEHT），将城市移动与拥堵信息融入基于Transformer的架构中。该方法将主要网络通信特征与次要城市移动特征分离，并在Transformer编码器中引入低秩适配（LoRA）以减少可训练参数，同时保持高预测精度。多模态融合策略将外部移动与拥堵特征注入解码器，进一步提升流量预测性能。在Telecom Italia Milan数据集及多个合成拥堵场景下的实验表明，PEHT在RMSE、MAE和R²指标上均优于现有先进基线。代码已开源。
原文摘要 · Abstract (English)
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.
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