Research on Traffic Data Recovery Based on Tensor Filling and Tensor Matrix Association Analysis
| Autor: | Jun Zhou, Shengbao Yang, Wei He, Jing He, Yi Xiang, Bingbing Song, Jiangcheng Xie |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Stability (learning theory) 02 engineering and technology computer.software_genre Missing data Field (computer science) Data recovery Data modeling 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Point (geometry) Tensor Data mining business Intelligent transportation system computer |
| Zdroj: | 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). |
| DOI: | 10.1109/ichci51889.2020.00041 |
| Popis: | Traffic data is the data foundation for smart transportation construction. However, due to inclement weather and equipment damage, there are often data missing during the collection of traffic data, which severely restricts smart transportation construction progress. Therefore, traffic data recovery has become an urgent problem in the field of intelligent transportation. Aiming at the problem that the recovery accuracy of existing traffic data recovery methods declines sharply under extreme missing conditions, this paper proposes a traffic data recovery model based on tensor filling and tensor matrix association analysis. The experimental results combined with real taxi GPS positioning data and point of interesting (POI) data of Kunming show that the traffic data recovery model proposed in this paper can significantly improve the recovery accuracy of missing data and maintain good stability in the case of extreme data missing. |
| Databáze: | OpenAIRE |
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