Parameter Efficient Hybrid Transformer (PEHT) for Network Traffic Prediction via Dynamic Urban Congestion Integration
2026-06-26 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors developed a new method called PEHT to better predict mobile network traffic in cities, which is hard because many factors affect demand. They used a type of AI model called a Transformer, making it more efficient by reducing the amount of training needed and separating city movement data from network data. Their approach also combines different information sources to improve predictions. Tests show that PEHT predicts traffic more accurately than existing methods.
network traffic predictionTransformerurban mobilitycongestion dynamicsLow-Rank Adaptation (LoRA)multimodal fusionresource allocationRMSEMAER squared (R²)
Authors
Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast
Abstract
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.