Efficient missing data imputing for traffic flow by considering temporal and spatial dependence
By Li Li, Yuebiao Li, Zhiheng Li. 2013
303 citations recorded by the source corpus.
Subfields: Building and Construction, Civil and Structural Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/7d8b442ac42d4463863b679f1b2d178b50f91d4a
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
- Lijun Sun
- Xinyu Chen
- Afshin Abadi
- Alexandre M. Bayen
- Takahiko Kusakabe
- Petros A. Ioannou
- Tooraj Rajabioun
- Yasuo Asakura
In Civil and Structural Engineering
In Transportation
- Lijun Sun
- Xinyu Chen
- Afshin Abadi
- Alexandre M. Bayen
- Takahiko Kusakabe
- Petros A. Ioannou
- Tooraj Rajabioun
- Yasuo Asakura
Citations in the corpus
- A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation (2019, background): “DA (daily average) fills the missing value with an average of observed data (over different days) for the same road segment and the same time window (Li et al., 2013). kNN is another baseline method where the neighbors refers to road segments.”
- Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data (2015, methodology): “Moreover, other statistical and probabilistic methods are used to address the missing traffic data problem [28]–[34].”
- Traffic state estimation on highway: A comprehensive survey (2017, methodology): “For example, van Lint et al. (2008); Ngoduy (2008) proposed multi-class TSE methods, Chapter 4 of Yuan (2013) proposed a multi-class TSE method in Lagrangian coordinates, and Lovisari et al.”