An efficient realization of deep learning for traffic data imputation

By Yanjie Duan, Yisheng Lv, Yu-Liang Liu, Feiyue Wang. 2016

313 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/2163484c0c284675f363aa0078541f00574b191a

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. Lijun Sun
2. Xinyu Chen
3. Alexandre M. Bayen
4. Takahiko Kusakabe
5. Yasuo Asakura
6. Toru Seo
7. Zhaocheng He

In Control and Systems Engineering

1. Alexandre M. Bayen
2. Takahiko Kusakabe
3. Yasuo Asakura
4. Toru Seo

In Transportation

1. Lijun Sun
2. Xinyu Chen
3. Alexandre M. Bayen
4. Takahiko Kusakabe
5. Yasuo Asakura
6. Toru Seo

Citations in the corpus

• A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation (2019, background): “Kolda and Bader (2009) gives a very comprehensive review of tensor decomposition and its applications.”
• Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, unknown): “Application domains Related works Transportation [177], [187], [116], [31], [29], [6], [32], [127], [53], [112], [26], [105], [166], [82], [23], [66], [108], [126], [81], [139], [78], [128], [146], [148], [72], [98], [189], [22], [24], [99], [96], [16], [97], [85], [15], [164], [165], [95], [140], [”
• Traffic state estimation on highway: A comprehensive survey (2017, methodology): “(2003) used a second-order traffic flow model to combine probe vehicle data with stationary data. Work et al. (2008, 2010) developed a first-order traffic flow model whose state variable is speed. Kesting and Treiber (2009) introduced a shock wave detection method based on probe vehicle data. van Li”
• Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, unknown): “sentations [70], [25].”