Summary of A Prompt Refinement-based Large Language Model For Metro Passenger Flow Forecasting Under Delay Conditions, by Ping Huang et al.
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions
by Ping Huang, Yuxin He, Hao Wang, Jingjing Chen, Qin Luo
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed passenger flow forecasting framework uses large language models (LLMs) to overcome the challenges of conventional models in accurately predicting passenger flow in metro systems under delay conditions. By synthesizing an LLM with carefully designed prompt engineering, the framework enables the model to understand delay event information and historical passenger flow data patterns. The prompt engineering consists of two stages: systematic prompt generation and refinement using the multidimensional Chain of Thought (CoT) method. Experimental results on real-world datasets from Shenzhen metro in China demonstrate that the proposed model performs well in forecasting passenger flow under delay conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Accurate short-term forecasts are crucial for emergency response and service recovery in metro systems, especially during delays. However, current models struggle to capture the complex impacts of delays due to limited data. To address this challenge, researchers propose a framework that combines large language models (LLMs) with prompt engineering. This approach enables the model to understand delay information and patterns from historical passenger flow data. The framework is tested on real-world datasets and shows promising results. |
Keywords
» Artificial intelligence » Prompt
