TrafficGPT is a deep-learning foundation model designed to tackle complex challenges in network traffic analysis and generation. By leveraging generative pre-training with a linear attention mechanism, it expands the effective token window from the traditional 512-token limit to 12,032 tokens.
While the original TrafficGPT research utilized a 99:1 train-test split (99% for pre-training, 1% for testing), this open-source version employs a standard 80:20 split.
TrafficGPT(12k) consistently outperforms existing state-of-the-art models[cite: 16, 281].
Measured using Jensen-Shannon Divergence (JSD), where lower values indicate closer similarity to real traffic.
1@article{qu2024trafficgpt,
2 title={TrafficGPT: Breaking the Token Barrier for Efficient Long Traffic Analysis and Generation},
3 author={Qu, Jian and Ma, Xiaobo and Li, Jianfeng},
4 journal={arXiv preprint arXiv:2403.05822},
5 year={2024}
6}
