Journal
MATHEMATICS
Volume 10, Issue 14, Pages -Publisher
MDPI
DOI: 10.3390/math10142544
Keywords
missing data imputation; time series analysis; missing pattern
Categories
Funding
- National Natural Science Foundation of China [52131204]
- Shanghai Sailing Program [22YF1452600, 22YF1452700]
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This article analyzes traffic temporal data imputation methods, reviewing research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets. Among different testing conditions, probabilistic principal component analysis performs the best.
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.
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