Summary of Spatio-temporal Road Traffic Prediction Using Real-time Regional Knowledge, by Sumin Han et al.
Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge
by Sumin Han, Jisun An, Dongman Lee
First submitted to arxiv on: 23 Aug 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 mid-term road traffic prediction in transportation services like car-sharing and ride-hailing. The existing approaches have focused on short-term prediction of how micro-traffic events affect adjacent roads, whereas this study incorporates regional knowledge such as POIs, road characteristics, and real-time social events to improve predictions. The proposed method embeds region-level knowledge using satellite images, real-time LTE access traces, and POIs via a dynamic convolutional and temporal attention-based module. This knowledge is then converted into road-level information using bipartite spatial transform attention. Experimental results show that the model outperforms baselines on real-world road traffic prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict road traffic in car-sharing and ride-hailing services better by combining different types of information. It shows how to use real-time data like phone location and what’s happening around roads to make more accurate predictions. The method is tested on real-world data and performs well, showing that it can be used to improve traffic prediction. |
Keywords
» Artificial intelligence » Attention
