Enhanced application of principal component analysis in machine learning for imputation of missing traffic data

Yoon Young Choi, Heeseung Shon, Young Ji Byon, Dong Kyu Kim, Seungmo Kang

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Missing value imputation approaches have been widely used to support and maintain the quality of traffic data. Although the spatiotemporal dependency-based approaches can improve the imputation performance for large and continuous missing patterns, additionally considering traffic states can lead to more reliable results. In order to improve the imputation performances further, a section-based approach is also needed. This study proposes a novel approach for identifying traffic-states of different spots of road sections that comprise, namely, a section-based traffic state (SBTS), and determining their spatiotemporal dependencies customized for each SBTS, for missing value imputations. A principal component analysis (PCA) was employed, and angles obtained from the first principal component were used to identify the SBTSs. The pre-processing was combined with a support vector machine for developing the imputation model. It was found that the segmentation of the SBTS using the angles and considering the spatiotemporal dependency for each state by the proposed approach outperformed other existing models.

Original languageEnglish
Article number2149
JournalApplied Sciences (Switzerland)
Issue number10
Publication statusPublished - 2019 May 1


  • Machine learning
  • Missing value imputation
  • Principal component analysis
  • Support vector machine

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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