Summary of Large-scale Demand Prediction in Urban Rail Using Multi-graph Inductive Representation Learning, by Dang Viet Anh Nguyen et al.
Large-Scale Demand Prediction in Urban Rail using Multi-Graph Inductive Representation Learning
by Dang Viet Anh Nguyen, J. Victor Flensburg, Fabrizio Cerreto, Bianca Pascariu, Paola Pellegrini, Carlos Lima Azevedo, Filipe Rodrigues
First submitted to arxiv on: 28 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 The proposed Multi-Graph Inductive Representation Learning (mGraphSAGE) model is an Origin-Destination (OD) demand prediction framework designed for large-scale Urban Rail Transit (URT) networks. The model incorporates multiple graphs, leveraging temporal and spatial correlations between OD pairs to enhance prediction results while ensuring scalability. It also accounts for operational uncertainties such as train delays and cancellations as inputs in demand prediction. The mGraphSAGE model is validated on three different scales of the URT network in Copenhagen, Denmark, outperforming reference machine learning methods. Its performance gap with other methods improves during periods with train cancellations and delays, demonstrating its ability to leverage system reliability information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict how many people will travel from one place to another on urban rail networks. It uses special graphs that show relationships between different places and times of day. This helps the model make better predictions by taking into account things like when trains are delayed or cancelled. The researchers tested their model on three parts of the Copenhagen railway network and found it worked better than other methods. |
Keywords
» Artificial intelligence » Machine learning » Representation learning
