Multi-view Low Rank Representation for Multi-Source Traffic Data Completion
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Intelligent Transportation System (ITS) has been widely applied in major cities to relieve congestion and decrease accidents. However, the hardware failure of detectors or transformation failure of data cause data loss, which seriously decreases the performance of ITS. How to ensure the completeness of observed traffic data becomes is a current key problem. Recently, the low rank constraint which can exploit the global relation hidden in data has been successfully used in matrix completion, such as the classic robust principal component analysis (RPCA) and its variants. The spatio-temporal correlation among traffic data make traffic data contain low rank property; therefore, we naturally apply the low rank constraint on traffic data completion. In addition, most traffic detectors installed on the road can collect various types of traffic data, so-called multi-source traffic data. Due to describing the same traffic condition, these various type of traffic data usually have similar intrinsic structure. Therefore, we consider fuse these various type of traffic data to complete the missing data. In this paper, we propose multi-view low-rank representation model for multi-source data completion and provide an efficient optimization algorithm. To variety the performance of the proposed method, some traditional traffic data completion methods are compared with our method on a highway microwave dataset. The experimental results show that our proposed method is obviously superior to other state-of-the-art traffic data completion methods.
KeywordsTraffic data Low-rank representation Matrix completion Multi-source data
The research project is supported by a grant (XDPB12) from the Chinese Academy of Sciences, and also partially supported by the National Natural Science Foundation of China under Grant No. 61876144.
- 3.Chen, J., Shao, J.: Nearest neighbor imputation for survey data. J. Off. Stat. 16(2), 113–132 (2000)Google Scholar
- 4.Qu, L., Zhang, Y., Hu, J., Jia, L., Li, L.: A bpca based missing value imputing method for traffic flow volume data. In: IEEE Intelligent Vehicles Symposium, pp. 985–990 (2008)Google Scholar
- 9.Wright, J., Ganesh, A., Rao, S., Peng, Y., Ma, Y.: Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization. In: Advances in Neural Information Processing Systems, vol. 22 (2009)Google Scholar
- 10.Li, P., Feng, J., Jin, X., Zhang, L., Xu, X., Yan, S.: Online robust low-rank tensor learning. In: International Joint Conference on Artificial Intelligence (2017)Google Scholar
- 11.Lin, Z., Liu, R., Su, Z.: Linearized alternating direction method with adaptive penalty for low rank representation. In: Advances in Neural Information Processing Systems, vol. 23 (2011)Google Scholar