Summary of Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends, by Hourun Li et al.
Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends
by Hourun Li, Yusheng Zhao, Zhengyang Mao, Yifang Qin, Zhiping Xiao, Jiaqi Feng, Yiyang Gu, Wei Ju, Xiao Luo, Ming Zhang
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 reviews the applications of Graph Neural Networks (GNNs) in six emerging Intelligent Transportation System (ITS) research areas: traffic forecasting, vehicle control system, traffic signal control, transportation safety, demand prediction, and parking management. GNNs have demonstrated excellent performance in addressing complex problems in ITS, but current research mostly focuses on traffic forecasting, with other domains receiving less attention. The paper aims to summarize the methodologies, features, and contributions of 36 graph-related studies from 2018 to 2023, highlighting challenges and potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are a type of artificial intelligence that can help make roads safer and traffic smoother. Right now, there’s a lot of focus on using GNNs for traffic forecasting, which means predicting what the traffic will be like in the future. But this technology can also be used for other important transportation problems, like keeping cars from crashing and making sure there are enough parking spots available. This paper looks at six areas where GNNs might make a big difference: traffic forecasting, vehicle control systems, traffic signal control, transportation safety, demand prediction, and parking management. It shows what each of these applications has achieved so far and suggests new ways to use GNNs in the future. |
Keywords
* Artificial intelligence * Attention
