PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach

By Li Qu, Jianming Hu, Li Li, Yi Zhang. IEEE transactions on intelligent transportation systems (Print), 2009

416 citations recorded by the source corpus.

Subfields: Building and Construction, Statistics and Probability, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2ea415f7c71f1bd8f437018b3a070b6dbeb2fde8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Sherali Zeadally

In Statistics and Probability

1. Fei-Yue Wang
2. Xin Xu
3. Wei-Hua Lin
4. Sherali Zeadally
5. Cheng Chen
6. Junping Zhang
7. Kunfeng Wang

In Signal Processing

1. Fei-Yue Wang
2. Xin Xu
3. Wei-Hua Lin
4. Cheng Chen
5. Junping Zhang
6. Kunfeng Wang

Citations in the corpus

• Sensor Technologies for Intelligent Transportation Systems (2018, methodology): “…design and development of algorithms that analyze the data in order to: (i) perform data cleansing to remove abnormal data gathered by sources [109,110]; (ii) remove some redundant features from the original feature space but preserves the interpretability of the remaining features [111,112]; and…”
• Data-Driven Intelligent Transportation Systems: A Survey (2011, methodology): “[100] introduced probabilistic principal component analysis (PPCA)-based missing data imputation, where PPCA is used to capture the main structure, and”
• A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation (2019, neutral): “Recently, factorization-based approaches—in particular tensor decomposition—have attracted more and more attention and have been tested in many empirical works (e.g., Qu et al., 2009; Asif et al., 2013b; Li et al., 2013; Tan et al., 2013; Asif et al., 2016; Goulart et al., 2017).”