bonaldli/bgcp_imputation

Bayesian Tensor Decomposition Approach for Incomplete Traffic Data Imputation

BGCP

Bayesian Gaussian Tensor Decomposition Approach for Incomplete Traffic Data Imputation. [Python code]

Overview

With the development and application of intelligent transportation systems, large quantities of urban traffic data are collected on a continuous basis from various sources, such as loop detectors, cameras, and floating vehicles. These data sets capture the underlying states and dynamics of transportation networks and the whole system and become beneficial to many traffic operation and management applications, including routing, signal control, travel time prediction, and so on. However, the missing data problem is inevitable when collecting traffic data from intelligent transportation systems.

Urban traffic speed data set of Guangzhou, China

Publicly available at our Zenodo repository!

(a) Time series of actual and estimated speed within two weeks from August 1 to 14.

(b) Time series of actual and estimated speed within two weeks from September 12 to 25.

Figure 1: The imputation performance of BGCP (CP rank r=15 and missing rate α=30%) under the fiber missing scenario with third-order tensor representation, where the estimated result of road segment #1 is selected as an example. In the both two panels, red rectangles represent fiber missing (i.e., speed observations are lost in a whole day).

Selected References

• Bayesian matrix factorization

  • Ruslan Salakhutdinov, Andriy Mnih, 2008. Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo. Proceedings of the 25th International Conference on Machine Learning (ICML 2008), Helsinki, Finland.

  • Li Li, Yuebiao Li, Zhiheng Li, 2013. Efficient Missing Data Imputing for Traffic Flow by Considering Temporal and Spatial Dependence. Transportation Research Part C: Emerging Technologies, 34: 108-120.

• Bayesian tensor factorization

  • Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G. Carbonell, 2010. Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization. Proceedings of the 2010 SIAM International Conference on Data Mining. SIAM, pp. 211-222.

  • Qibin Zhao, Liqing Zhang, Andrzej Cichocki, 2015. Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1751-1763.

  • Qibin Zhao, Liqing Zhang, Andrzej Cichocki, 2015. Bayesian Sparse Tucker Models for Dimension Reduction and Tensor Completion. arXiv.

  • Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David B. Dunsun, Lawrence Carin, 2014. Scalable Bayesian Low-rank Decomposition of Incomplete Multiway Tensors. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), Beijing, China.

• Low-rank tensor completion

  • Ji Liu, Przemyslaw Musialski, Peter Wonka, Jieping Ye, 2013. Tensor Completion for Estimating Missing Values in Visual Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1): 208-220.

  • Bin Ran, Huachun Tan, Yuankai Wu, Peter J. Jin, 2016. Tensor Based Missing Traffic Data Completion with Spatial–temporal Correlation. Physica A: Statistical Mechanics and its Applications, 446: 54-63.

Our Publication

• Xinyu Chen, Zhaocheng He, Lijun Sun, 2019. A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 98: 73-84.

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