NDLI: Robust and flexible strategy for missing data imputation in intelligent transportation system

Content Provider	IET Digital Library
Author	Li, Linchao Zhang, Jian Yang, Fan Ran, Bin
Abstract	Rich and complete data play a fundamental role in intelligent traffic management and control applications. A great volume of missing data is found in the intelligent transportation system. In this paper, the authors introduce an ensemble strategy to handle the missing values. The proposed strategy is a general framework that different models, whether linear, neural networks, or other, can be applied. In this strategy, missing values are first computed by the forward and backward models, and their results are combined to recover the incomplete raw data. Then, the models are iterated for several times to enhance the accuracy. Three commonly used imputation models are tested in the proposed strategy using the data from real world. The results indicate that the proposed strategy can significantly improve the accuracy of the imputation with different missing types and during different traffic states. Moreover, the increase of the iteration is capable to improve the performance of the models.
Starting Page	151
Ending Page	157
Page Count	7
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 2, Mar (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0273
Journal	IET Intelligent Transport Systems
Publisher Date	2017-11-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Backward Model Control Application Ensemble Strategy Flexible Strategy Forward Model Incomplete Raw Data Recovery Intelligent Traffic Management Intelligent Transport System Missing Data Imputation Neural Computing Technique Neural Nets Neural Network Road Traffic Traffic Engineering Computing Traffic State
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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