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A Comprehensive Survey on Traffic Missing Data Imputation
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AI
DOI：10.1109/TITS.2024.3478816.png)
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Abstract
En 中文
Intelligent Transportation Systems (ITS) are essential and play a key role in improving road safety, reducing congestion, optimizing traffic flow and facilitating the development of smart cities. The collection of data from ITS and its transformation into information is challenged by the presence of missing data in datasets. Timely and effective handling of missing data is crucial to facilitate intelligent decision making. In response to this need, numerous researchers have proposed various techniques for dealing with missing data, with commendable results in different scenarios. Therefore, this survey aims to provide a well-organized and thorough overview of the research related to imputation of missing data in traffic. Our purposes are at least fourfold. First, we discuss the background of missing data and highlight the value of traffic data imputation. Second, we present a comprehensive list of missing patterns, open data, widely used evaluation metrics and performance goals for this issue. Third, we categorize related studies into three parts: interpolation-based, statistical learning-based, prediction-based methods, and provide a secondary classification within each category to better understand the characteristics and limitations of each method. Finally, we identify future research directions to advance the understanding of traffic data imputation.
Keywords:
Imputation
Data models
Measurement
Transportation
Accuracy
Reviews
Predictive models
Fluctuations
Benchmark testing
Traffic data imputation
intelligent transportation systems
deep learning
surveys
surveys
Journal
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