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Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms

Author

Listed:
  • Tuo Sun

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Shihao Zhu

    (Anting Shanghai International Automobile City, Shanghai 201804, China)

  • Ruochen Hao

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

  • Bo Sun

    (Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore)

  • Jiemin Xie

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510275, China)

Abstract

A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.

Suggested Citation

  • Tuo Sun & Shihao Zhu & Ruochen Hao & Bo Sun & Jiemin Xie, 2022. "Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms," Mathematics, MDPI, vol. 10(14), pages 1-22, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2544-:d:868379
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    References listed on IDEAS

    as
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