Summary of Tpllm: a Traffic Prediction Framework Based on Pretrained Large Language Models, by Yilong Ren et al.
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
by Yilong Ren, Yue Chen, Shuai Liu, Boyue Wang, Haiyang Yu, Zhiyong Cui
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel traffic prediction framework, TPLLM, leverages Large Language Models (LLMs) to achieve accurate predictions and good generalization ability in areas with limited historical traffic data. By combining Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs), TPLLM extracts sequence features and spatial features from traffic data, which are then integrated as inputs for LLMs. The LoRA fine-tuning approach minimizes computational demands and facilitates efficient learning. Experimental results on two real-world datasets show commendable performance in full-sample and few-shot prediction scenarios, supporting the development of Intelligent Transportation Systems (ITS) in regions with scarce historical traffic data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TPLLM is a new way to predict traffic that uses big language models. Traffic prediction is important for managing traffic well. Right now, we can get more accurate predictions by using more training data. But collecting and storing this data is expensive. So, it’s hard to make models that work well in places with limited data. This new framework uses a combination of convolutional neural networks and graph convolutional networks to extract important features from traffic data. Then, it uses large language models to make predictions. The approach also helps reduce the need for lots of computation. Tests on real-world datasets show that this method works well, even when there’s limited data. |
Keywords
* Artificial intelligence * Few shot * Fine tuning * Generalization * Lora
