Summary of Enhancing Traffic Prediction with Textual Data Using Large Language Models, by Xiannan Huang
Enhancing Traffic Prediction with Textual Data Using Large Language Models
by Xiannan Huang
First submitted to arxiv on: 10 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This study proposes a novel approach for traffic prediction that integrates non-numerical contextual information like weather into models. The method utilizes large language models to process textual information and obtain embeddings, which are then combined with historical traffic data and inputted into traditional spatiotemporal forecasting models. The study investigates two types of special scenarios: regional-level and node-level, showing a significant improvement in prediction accuracy according to the experiment on New York Bike dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make transportation planning more efficient by predicting traffic patterns better. It uses big language models to understand weather and other information that affects traffic, and then combines this with past traffic data to make predictions. The results show that this approach is really good at predicting traffic, especially in specific areas or nodes. |
Keywords
» Artificial intelligence » Spatiotemporal
