Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916400307634176 |
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| author | Li, Hourun Zhao, Yusheng Mao, Zhengyang Qin, Yifang Xiao, Zhiping Feng, Jiaqi Gu, Yiyang Ju, Wei Luo, Xiao Zhang, Ming |
| author_facet | Li, Hourun Zhao, Yusheng Mao, Zhengyang Qin, Yifang Xiao, Zhiping Feng, Jiaqi Gu, Yiyang Ju, Wei Luo, Xiao Zhang, Ming |
| contents | Intelligent Transportation System (ITS) is crucial for improving traffic congestion, reducing accidents, optimizing urban planning, and more. However, the complexity of traffic networks has rendered traditional machine learning and statistical methods less effective. With the advent of artificial intelligence, deep learning frameworks have achieved remarkable progress across various fields and are now considered highly effective in many areas. Since 2019, Graph Neural Networks (GNNs) have emerged as a particularly promising deep learning approach within the ITS domain, owing to their robust ability to model graph-structured data and address complex problems. Consequently, there has been increasing scholarly attention to the applications of GNNs in transportation, which have demonstrated excellent performance. Nevertheless, current research predominantly focuses on traffic forecasting, with other ITS domains, such as autonomous vehicles and demand prediction, receiving less attention. This paper aims to review the applications of GNNs across six representative and emerging ITS research areas: traffic forecasting, vehicle control system, traffic signal control, transportation safety, demand prediction, and parking management. We have examined a wide range of graph-related studies from 2018 to 2023, summarizing their methodologies, features, and contributions in detailed tables and lists. Additionally, we identify the challenges of applying GNNs in ITS and propose potential future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00713 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends Li, Hourun Zhao, Yusheng Mao, Zhengyang Qin, Yifang Xiao, Zhiping Feng, Jiaqi Gu, Yiyang Ju, Wei Luo, Xiao Zhang, Ming Machine Learning Intelligent Transportation System (ITS) is crucial for improving traffic congestion, reducing accidents, optimizing urban planning, and more. However, the complexity of traffic networks has rendered traditional machine learning and statistical methods less effective. With the advent of artificial intelligence, deep learning frameworks have achieved remarkable progress across various fields and are now considered highly effective in many areas. Since 2019, Graph Neural Networks (GNNs) have emerged as a particularly promising deep learning approach within the ITS domain, owing to their robust ability to model graph-structured data and address complex problems. Consequently, there has been increasing scholarly attention to the applications of GNNs in transportation, which have demonstrated excellent performance. Nevertheless, current research predominantly focuses on traffic forecasting, with other ITS domains, such as autonomous vehicles and demand prediction, receiving less attention. This paper aims to review the applications of GNNs across six representative and emerging ITS research areas: traffic forecasting, vehicle control system, traffic signal control, transportation safety, demand prediction, and parking management. We have examined a wide range of graph-related studies from 2018 to 2023, summarizing their methodologies, features, and contributions in detailed tables and lists. Additionally, we identify the challenges of applying GNNs in ITS and propose potential future research directions. |
| title | Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2401.00713 |