Summary of Leveraging Intra-period and Inter-period Features For Enhanced Passenger Flow Prediction Of Subway Stations, by Xiannan Huang et al.
Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations
by Xiannan Huang, Chao Yang, Quan Yuan
First submitted to arxiv on: 16 Oct 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 MPSTN model is a novel approach for short-term passenger flow prediction of subway stations, which leverages features from different periods to accurately predict changes in passenger volume. The model transforms one-dimensional time series data into two-dimensional matrices based on periods, allowing the utilization of image processing techniques, specifically CNNs, to integrate features from different periods. The MPSTN model incorporates a CNN module to extract temporal information and a GNN module to integrate spatial information. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods for spatiotemporal data prediction using a publicly available dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to predict how many people will be at a subway station in the short-term future, which is important because it can help station staff prepare for changes in passenger volume. The model uses information from different time periods and different stations to make more accurate predictions. It combines techniques used in image processing and graph neural networks to analyze this data. The results show that this approach works better than other methods using a publicly available dataset. |
Keywords
» Artificial intelligence » Cnn » Gnn » Spatiotemporal » Time series
