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ST-LLC: A Large-Scale and Lightweight Model Collaboration Framework for Spatio-Temporal Traffic Prediction
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DOI：10.1109/mnet.2026.3664410.png)
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Abstract
En 中文
Sensor-cloud technology is a key enabler for transportation of smart cities, providing large-scale traffic data for analysis in increasingly complex traffic systems. However, spatio-temporal traffic data poses significant computational challenges, making it critical to fully exploit such data and explore cloud-edge collaborative intelligence in data-driven scenarios. To address this, we propose a spatio-temporal large-scale and lightweight model collaboration framework (ST-LLC), which distributes computational workloads across the cloud and edge to achieve accurate traffic prediction. In this framework, a lightweight graph network deployed at the edge captures shortterm dynamics, while dual auto-encoders in the cloud extract long-term traffic priors. These heterogeneous features are then integrated through a cross-feed attention (CFA) mechanism, and a large language model (LLM) is designed to transfer domain knowledge and enhance adaptability. We evaluate the proposed framework on three real-world public datasets. Extensive experimental results demonstrate that ST-LLC effectively integrates cloud-edge traffic sequence features and achieves superior prediction accuracy compared with state-of-the-art methods. Finally, we discuss practical challenges such as communication latency and data security, and outline future directions.
Keywords:
Cloud computing
Collaboration
Computational modeling
Smart cities
Predictive models
Feature extraction
Accuracy
Data models
Computer architecture
Large-scale systems
Sensor systems and applications
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