Summary of Delayptc-llm: Metro Passenger Travel Choice Prediction Under Train Delays with Large Language Models, by Chen Chen et al.
DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train Delays with Large Language Models
by Chen Chen, Yuxin He, Hao Wang, Jingjing Chen, Qin Luo
First submitted to arxiv on: 28 Sep 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 proposed framework, DelayPTC-LLM, utilizes large language models (LLMs) to predict passenger travel choices under metro delays. The approach leverages the strengths of LLMs in text processing, understanding, and small-sample learning to tackle the challenges of sparse datasets and sample imbalance. A comparative analysis with traditional prediction models demonstrates the superior capability of LLMs in handling complex data commonly encountered during transportation disruptions. The framework’s well-designed prompting engineering guides the LLM in making rationalized predictions about travel choices, considering passenger heterogeneity and delay event features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Passenger travel choices are crucial for Urban Rail Transit (URT) network operation. When delays occur, understanding how passengers will adapt is vital for emergency response and service recovery. This paper proposes a new framework using large language models (LLMs) to predict travel choices under metro delays. The approach handles sparse datasets and sample imbalance challenges. Real-world data from Shenzhen Metro shows the LLM’s accuracy and potential to provide actionable insights. |
Keywords
» Artificial intelligence » Prompting
