Graph neural network driven traffic prediction technology:review and challenge
With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-dr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2021-12-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00235/ |
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| _version_ | 1841531206080921600 |
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| author | Yi ZHOU Shuting HU Wei LI Nan CHENG Ning LU Xuemin(Sherman) SHEN |
| author_facet | Yi ZHOU Shuting HU Wei LI Nan CHENG Ning LU Xuemin(Sherman) SHEN |
| author_sort | Yi ZHOU |
| collection | DOAJ |
| description | With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated. |
| format | Article |
| id | doaj-art-db10076fee6a404eaed6d206b46347bc |
| institution | Kabale University |
| issn | 2096-3750 |
| language | zho |
| publishDate | 2021-12-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 物联网学报 |
| spelling | doaj-art-db10076fee6a404eaed6d206b46347bc2025-01-15T02:53:07ZzhoChina InfoCom Media Group物联网学报2096-37502021-12-01511659647550Graph neural network driven traffic prediction technology:review and challengeYi ZHOUShuting HUWei LINan CHENGNing LUXuemin(Sherman) SHENWith the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00235/traffic predictiongraph neural networksspatial-temporal correlationsynchronous convolutiongraph at-tention networks |
| spellingShingle | Yi ZHOU Shuting HU Wei LI Nan CHENG Ning LU Xuemin(Sherman) SHEN Graph neural network driven traffic prediction technology:review and challenge 物联网学报 traffic prediction graph neural networks spatial-temporal correlation synchronous convolution graph at-tention networks |
| title | Graph neural network driven traffic prediction technology:review and challenge |
| title_full | Graph neural network driven traffic prediction technology:review and challenge |
| title_fullStr | Graph neural network driven traffic prediction technology:review and challenge |
| title_full_unstemmed | Graph neural network driven traffic prediction technology:review and challenge |
| title_short | Graph neural network driven traffic prediction technology:review and challenge |
| title_sort | graph neural network driven traffic prediction technology review and challenge |
| topic | traffic prediction graph neural networks spatial-temporal correlation synchronous convolution graph at-tention networks |
| url | http://www.wlwxb.com.cn/zh/article/doi/10.11959/j.issn.2096-3750.2021.00235/ |
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