The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach
Abstract
The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.
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Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
Short-term traffic forecasting: where we are and where we’re going
Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2014.01.005 · Cited by 1,132
Sensor Technologies for Intelligent Transportation Systems
Guerrero-Ibáñez, J., Zeadally, S., & Contreras-Castillo, J. (2018) · Sensors · doi:10.3390/s18041212 · Cited by 637
A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation
Chen, X., He, Z., & Sun, L. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.11.003 · Cited by 373
An efficient realization of deep learning for traffic data imputation
Duan, Y., Lv, Y., Liu, Y.-L., & Wang, F.-Y. (2016) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2016.09.015 · Cited by 316
Metadata
- Authors
- Qu, L., Hu, J., Li, L., & Zhang, Y.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2009-09
- Volume
- 10
- Issue
- 3
- Pages
- 512-522
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2163150789
- Cited by
- 441
- References
- 41
- FWCI
- 5.77
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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