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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2019  >  19(5): 180-190
CHEN Xiao-bo, CHEN Cheng, CHEN Lei, WEI Zhong-jie, CAI Ying-feng, ZHOU Jun-jie. Interpolation method of traffic volume missing data based on improved low-rank matrix completion[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 180-190. doi: 10.19818/j.cnki.1671-1637.2019.05.018
Citation: CHEN Xiao-bo, CHEN Cheng, CHEN Lei, WEI Zhong-jie, CAI Ying-feng, ZHOU Jun-jie. Interpolation method of traffic volume missing data based on improved low-rank matrix completion[J]. Journal of Traffic and Transportation Engineering, 2019, 19(5): 180-190. doi: 10.19818/j.cnki.1671-1637.2019.05.018
shu

Interpolation method of traffic volume missing data based on improved low-rank matrix completion

doi: 10.19818/j.cnki.1671-1637.2019.05.018
  • 1.

    School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China

  • 2.

    Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210003, Jiangsu, China

  • 3.

    Chery Automobile Co., Ltd., Wuhu 241009, Anhui, China

More Information
  • Author Bio:

    CHENXiao-bo(1982-), male, associate professor, PhD, xbchen82@gmail.com

  • Received Date: 2019-04-20
  • Publish Date: 2019-10-25
    Abstract
  • An improved low-rank matrix completion method was proposed to study the interpolation problem of road traffic volume missing data. The missing data in the traffic volume data matrix were interpolated in the first round by the low-rank matrix completion based on the nuclear norm. Hierarchical clustering algorithm was applied to classify traffic volume data into different clusters so that the data in the same cluster had strong correlation, while the data in different clusters had weak correlation. Low-rank matrix completion method was applied to each cluster to complete the second round interpolation for missing data. In order to reduce the impact of clustering number, the least square regression ensemble learning approach was proposed to combine the interpolation results under different clustering numbers, so as to obtain the final traffic volume data interpolation results. The interpolation errors of five methods were compared based on the highway traffic volume data in Portland, Oregon, USA, and the influences of different clustering numbers and distance metrics methods were analyzed. Analysis result shows that under the completely random missing pattern, when the missing rate is 10%-60%, the interpolation error reduces by 5.93%-9.11% compared with the traditional low-rank matrix completion model. Under the random and mixed missing patterns, the interpolation errors reduce by 8.32%-9.55% and 8.14%-9.20%, respectively. The integration of multiple interpolations under different clustering numbers can reduce the interpolation error by 2.62%-4.76% compared with the results under single clustering number. Therefore, under three data missing modes, the improved low-rank matrix completion method can reduce the interpolation error of traffic volume data effectively, and improve the effectiveness of traffic volume data after interpolation.

     

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