Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation
1
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
2
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
3
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(9), 2884; https://doi.org/10.3390/s18092884
Received: 1 August 2018
/
Revised: 28 August 2018
/
Accepted: 29 August 2018
/
Published: 31 August 2018
(This article belongs to the Section Intelligent Sensors)
The problem of missing values (MVs) in traffic sensor data analysis is universal in current intelligent transportation systems because of various reasons, such as sensor malfunction, transmission failure, etc. Accurate imputation of MVs is the foundation of subsequent data analysis tasks since most analysis algorithms need complete data as input. In this work, a novel MVs imputation approach termed as kernel sparse representation with elastic net regularization (KSR-EN) is developed for reconstructing MVs to facilitate analysis with traffic sensor data. The idea is to represent each sample as a linear combination of other samples due to inherent spatiotemporal correlation, as well as periodicity of daily traffic flow. To discover few yet correlated samples and make full use of the valuable information, a combination of l1-norm and l2-norm is employed to penalize the combination coefficients. Moreover, the linear representation among samples is extended to nonlinear representation by mapping input data space into high-dimensional feature space, which further enhances the recovery performance of our proposed approach. An efficient iterative algorithm is developed for solving KSR-EN model. The proposed method is verified on both an artificially simulated dataset and a public road network traffic sensor data. The results demonstrate the effectiveness of the proposed approach in terms of MVs imputation.
View Full-Text
Keywords:
sparse representation; elastic net; kernel method; missing data; imputation
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Chen, X.; Chen, C.; Cai, Y.; Wang, H.; Ye, Q. Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation. Sensors 2018, 18, 2884. https://doi.org/10.3390/s18092884
AMA Style
Chen X, Chen C, Cai Y, Wang H, Ye Q. Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation. Sensors. 2018; 18(9):2884. https://doi.org/10.3390/s18092884
Chicago/Turabian StyleChen, Xiaobo, Cheng Chen, Yingfeng Cai, Hai Wang, and Qiaolin Ye. 2018. "Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation" Sensors 18, no. 9: 2884. https://doi.org/10.3390/s18092884
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
