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

Tuo Sun, Shihao Zhu, Ruochen Hao, Bo Sun and Jiemin Xie
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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

Mathematics, 2022, vol. 10, issue 14, 1-22

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.

Keywords: missing data imputation; time series analysis; missing pattern (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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