U-STS-LLM: A Unified Spatio-Temporal Steered Large Language Model for Traffic Prediction and Imputation

Abstract

Accurate spatio-temporal cellular traffic analysis is fundamental to the reliable operation of modern mobile networks, as it underpins load forecasting for congestion avoidance and missing-data imputation for continuity despite transmission errors. While deeply connected, forecasting and imputation have historically evolved as separate sub-fields. Existing models are typically task-specific, decouple temporal and spatial modeling, and rely on cascaded designs that accumulate errors and latency. Meanwhile, adapting pre-trained LLMs for this domain often leaves spatial structure under-guided and tends to overfit on limited data. To bridge these gaps, we propose U-STS-LLM, a unified framework built on a partially frozen spatio-temporally attentive (PFSTA) LLM backbone. A Dynamic Spatio-Temporal Attention Bias Generator synthesizes a persistent functional graph with transient nodal states to steer attention, while a lightweight parallel branch yields a stable local estimate fused via a Gated Adaptive Fusion, confining the LLM to residual correction. Trained under a unified multi-task objective, the model attains state-of-the-art results with low trainable-parameter overhead. Extensive experiments on real-world cellular traffic datasets demonstrate the effectiveness and transferability of U-STS-LLM, achieving average RMSE improvements of 23.7% over the strongest baselines in forecasting and 13.1% in imputation, with further gains exceeding 33% in zero-shot transfer to unseen network regions.