Summary of Congestion Forecast For Trains with Railroad-graph-based Semi-supervised Learning Using Sparse Passenger Reports, by Soto Anno et al.
Congestion Forecast for Trains with Railroad-Graph-based Semi-Supervised Learning using Sparse Passenger Reports
by Soto Anno, Kota Tsubouchi, Masamichi Shimosaka
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a novel approach for forecasting rail congestion using reports from passengers collected through a transit application. The authors address the challenge of limited reports due to passenger reluctance, which can result in sparse labeling data. They propose SURCONFORT, a semi-supervised method that leverages sparsely labeled data and many unlabeled data. This is achieved by adopting semi-supervised learning and designing a railway network-oriented graph for semi-supervised graph regularization. The authors demonstrate the effectiveness of their approach through empirical experiments with actual reporting data, showing improved forecasting performance by 14.9% over state-of-the-art methods under label sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rail congestion forecasting is crucial for efficient mobility in transport systems. This paper uses reports from passengers collected through a transit app to predict rail congestion. The challenge is that passengers are reluctant to report, making it hard to get enough data. To solve this, the authors created a new way of predicting using semi-supervised learning and a railway network graph. They tested their approach with real data and found it improved performance by 14.9% compared to other methods. |
Keywords
» Artificial intelligence » Regularization » Semi supervised
