Summary of Harnessing Llms For Cross-city Od Flow Prediction, by Chenyang Yu et al.
Harnessing LLMs for Cross-City OD Flow Prediction
by Chenyang Yu, Xinpeng Xie, Yan Huang, Chenxi Qiu
First submitted to arxiv on: 5 Sep 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 This paper proposes a novel method for predicting Origin-Destination (OD) flows across different cities using Large Language Models (LLMs). The approach leverages LLMs’ advanced semantic understanding and contextual learning capabilities to bridge the gap between cities with varied traffic conditions, urban layouts, and socio-economic factors. The proposed framework consists of four components: collecting OD training datasets from a source city, instruction-tuning the LLMs, predicting destination POIs in a target city, and identifying the locations that best match the predicted destination POIs. A new loss function is introduced that integrates POI semantics and trip distance during training. The model extracts high-quality semantic features from human mobility and POI data to understand spatial and functional relationships within urban spaces and capture interactions between individuals and various POIs. Experimental results demonstrate the superiority of this approach over state-of-the-art learning-based methods in cross-city OD flow prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps cities plan better transportation systems by predicting where people will go. Traditional models work well for one city, but not when applied to another. The new method uses special language models that can learn from many cities and understand how they’re different. It’s like a translator that can talk to different cities and figure out what people will do in each place. The model learns from data about where people go and why they go there. This helps it predict where people will go in the future. The results show that this new method is better than others at predicting OD flows across different cities. |
Keywords
» Artificial intelligence » Instruction tuning » Loss function » Semantics
