Summary of Joint Estimation and Prediction Of City-wide Delivery Demand: a Large Language Model Empowered Graph-based Learning Approach, by Tong Nie et al.
Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach
by Tong Nie, Junlin He, Yuewen Mei, Guoyang Qin, Guilong Li, Jian Sun, Wei Ma
First submitted to arxiv on: 30 Aug 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 In this paper, researchers tackle the complex problem of predicting urban delivery demand by developing a novel machine learning framework. The framework combines graph-based spatiotemporal learning with large language models (LLMs) to estimate and predict city-wide delivery demand. A neural network model is formalized to capture interaction between demand patterns in associated regions. The LLMs are used to extract geospatial knowledge encodings from unstructured locational data, which are then integrated into the demand predictor for cross-city generalization. Evaluation on two real-world datasets demonstrates that this approach outperforms state-of-the-art baselines in accuracy, efficiency, and transferability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Urban delivery demand is a big problem! Cities are getting busier and more complicated, making it hard to predict how many packages will need to be delivered. Some smart people wanted to find a way to make this process better using computers. They created a special program that can look at patterns in where people live and work, and use that information to predict delivery demand for whole cities. This is important because it helps companies like Amazon or UPS deliver things more efficiently. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Neural network » Spatiotemporal » Transferability
