Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method

In reality, readings of sensors on highways are usually missing at various unexpected moments due to some sensor or communication errors. These missing values do not only influence the real-time traffic monitoring but also prevent further traffic data mining. In this paper, we propose a multi-view l...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2019-08, Vol.20 (8), p.2933-2943
Main Authors: Li, Linchao, Zhang, Jian, Wang, Yonggang, Ran, Bin
Format: Article
Language:English
Subjects:
Algorithms
Collaborative filtering technique
Data mining
Data models
highway
Highways
Kernel functions
Learning
Learning systems
Logic gates
long-short term memory
Model accuracy
Parameter sensitivity
Performance enhancement
Performance evaluation
Regression analysis
Road transportation
Sensitivity analysis
Sensors
Support vector machines
support vector regression
Teaching methods
temporal and spatial views
Time series
Time series analysis
Traffic information
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Summary:In reality, readings of sensors on highways are usually missing at various unexpected moments due to some sensor or communication errors. These missing values do not only influence the real-time traffic monitoring but also prevent further traffic data mining. In this paper, we propose a multi-view learning method to estimate the missing values for traffic-related time series data. The model combines data-driven algorithms (long-short term memory and support vector regression) and collaborative filtering techniques. It can consider the local and global variation in temporal and spatial views to capture more information from the existing data. The estimations of missing values from four views are aggregated to obtain a final value with a kernel function. Data from a highway network are used to evaluate the performance of the proposed model in terms of accuracy, precision, and agreement. The results indicate that our proposed model outperforms other baselines, especially for block missing pattern with a high missing ratio. Furthermore, the sensitivity of the parameters is analyzed. We can conclude that combining different views can improve the performance of the imputation.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2869768