Volume 24 Issue 4
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(4): 243-258
ZHAO Yong-mei, DONG Yun-wei. Spatio-temporal traffic data prediction based on low-rank tensor completion[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 243-258. doi: 10.19818/j.cnki.1671-1637.2024.04.018
Citation: ZHAO Yong-mei, DONG Yun-wei. Spatio-temporal traffic data prediction based on low-rank tensor completion[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 243-258. doi: 10.19818/j.cnki.1671-1637.2024.04.018
shu

Spatio-temporal traffic data prediction based on low-rank tensor completion

doi: 10.19818/j.cnki.1671-1637.2024.04.018
  • 1.

    School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China

  • 2.

    School of Equipment Management and Unmanned Aerial Vehicle Engineering, Air Force Engineering University, Xi'an 710051, Shaanxi, China

Funds:

National Natural Science Foundation of China 62002381

More Information
  • Author Bio:

    ZHAO Yong-mei(1982-), female, associate professor, doctoral student, yong_zhao_2@163.com

    DONG Yun-wei(1968-), male, professor, PhD

  • Received Date: 2024-01-21
    Available Online: 2024-09-26
  • Publish Date: 2024-08-28
    Abstract
  • To dynamically evaluate traffic condition in real time, a traffic speed prediction model based on autoregressive regularization terms and Laplacian regularization terms was proposed. To improve the model's expression capability in global dimensions, a Laplace convolutional regularization term based on a low-rank tensor completion framework was introduced to represent the correlations of road segments. To improve the model's expression capability in local spatial dimensions, the time series trend-capturing capabilities of autoregressive models were utilized, and the short- and long-term expression capabilities of the models in the time dimension were improved to capture the spatio-temporal information of traffic data more effectively. The implementation of the truncated kernel norm as the low-rank tensor approximation model and the conversion of time- and frequency-domain signals leaded to improve the computation efficiency. An efficient low-rank Laplacian autoregressive tensor completion (LLATC) prediction method was developed by using the alternating direction multiplier method. Based on taxi speed data set and expressway traffic speed data set, the completion performances of the LLATC algorithm under different missing rates were systematically analyzed, and the prediction accuracy of the LLATC algorithm was compared with other baseline prediction algorithms. Research results show that under the random missing pattern with a missing rate of 20% to 70%, the mean absolute error (MAE) of the LLATC algorithm reduces by 2% to 6% compared to the traditional low-rank tensor completion models, and the MAE reduces by 4% to 22% compared to the traditional prediction methods. Under the non-random missing pattern, the MAE of the LLATC algorithm reduces by 2% to 6% compared to the traditional low-rank tensor completion models, and the MAE reduces by 13% to 25% compared to the traditional prediction methods. The finding indicates that the LLATC algorithm effectively reduces the completion error of traffic volume data, significantly enhances the prediction accuracy of traffic volume data under two kinds of missing data patterns, and simplifies the data processing workflow.

     

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