中国邮电高校学报(英文) ›› 2025, Vol. 32 ›› Issue (1): 48-60.doi: 10.19682/j.cnki.1005-8885.2025.0001

• Artificial Intelligence • 上一篇    下一篇

KNN spatio-temporal attention graph convolutional network for traffic flow repairing

张玺君,李喆   

  1. 兰州理工大学
  • 收稿日期:2024-03-18 修回日期:2024-09-16 出版日期:2025-02-28 发布日期:2025-02-28
  • 通讯作者: 张玺君 E-mail:zhangxijun198079@sina.com
  • 基金资助:
    国家自然科学基金项目;国家自然科学基金项目;甘肃省科技计划项目;甘肃省科技计划资助自然科学基金重点项目

KNN spatio-temporal attention graph convolutional network for traffic flow repairing

Zhe LI2   

  • Received:2024-03-18 Revised:2024-09-16 Online:2025-02-28 Published:2025-02-28
  • Supported by:
    National Natural Science Foundation of China;National Natural Science Foundation of China;Science and Technology Plan of Gansu Province;Provincial Science and Technology Program supported the key project of Natural Science Foundation of Gansu Province

摘要:

In the process of obtaining information from the actual traffic network, the incomplete data set caused by missing data reduces the validity of the data and the performance of the data-driven model. A traffic flow repair model based on a k-nearest neighbor ( KNN) spatio-temporal attention ( STA) graph convolutional network ( KAGCN) was proposed in this paper. Firstly, the missing data is initially interpolated by the KNN algorithm, and then the complete index set ( CIS) is constructed by combining the adjacency matrix of the network structure. Secondly, a STA mechanism is added to the CIS to capture the spatio-temporal correlation between the data. Then, the graph neural network ( GNN) is used to reconstruct the data by spatio-temporal correlation, and the reconstructed data set is used to correct and optimize the initial interpolation data set to obtain the final repair result. Finally, the PEMSD4 data set is used to simulate the missing data in the actual road network, and experiments are carried out under the missing rate of 30% , 50% , and 70% respectively. The results show that the mean absolute error ( MAE), root mean square error ( RMSE), and mean absolute percentage error ( MAPE) of the KAGCN model increased by at least 3.83% , 2.80% , and 5.33% , respectively, compared to the other baseline models at different deletion rates. It proves that the KAGCN model is effective in repairing the missing data of traffic flow.


关键词:

missing data repair, complete index set ( CIS ), interpolation-reconstruction, k-nearest neighbor ( KNN ) algorithm, spatio-temporal correlation


Abstract:

In the process of obtaining information from the actual traffic network, the incomplete data set caused by missing data reduces the validity of the data and the performance of the data-driven model. A traffic flow repair model based on a k-nearest neighbor ( KNN) spatio-temporal attention ( STA) graph convolutional network ( KAGCN) was proposed in this paper. Firstly, the missing data is initially interpolated by the KNN algorithm, and then the complete index set ( CIS) is constructed by combining the adjacency matrix of the network structure. Secondly, a STA mechanism is added to the CIS to capture the spatio-temporal correlation between the data. Then, the graph neural network ( GNN) is used to reconstruct the data by spatio-temporal correlation, and the reconstructed data set is used to correct and optimize the initial interpolation data set to obtain the final repair result. Finally, the PEMSD4 data set is used to simulate the missing data in the actual road network, and experiments are carried out under the missing rate of 30% , 50% , and 70% respectively. The results show that the mean absolute error ( MAE), root mean square error ( RMSE), and mean absolute percentage error ( MAPE) of the KAGCN model increased by at least 3.83% , 2.80% , and 5.33% , respectively, compared to the other baseline models at different deletion rates. It proves that the KAGCN model is effective in repairing the missing data of traffic flow.

Key words: missing data repair, complete index set ( CIS ), interpolation-reconstruction, k-nearest neighbor ( KNN ) algorithm, spatio-temporal correlation