High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
By Zhiyong Cui, Kristian C. Henrickson, Ruimin Ke, Yinhai Wang. 2018
82 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yoshua Bengio
- Sergey Levine
- Ruslan Salakhutdinov
- Trevor Darrell
- Zhe Gan
- Abhinav Kumar Gupta
- Mohit Bansal
- Kyunghyun Cho
Citations in the corpus
- Relational inductive biases, deep learning, and graph networks (2018, background): “…al., 2017), to predict the chemical properties of molecules (Duvenaud et al., 2015; Gilmer et al., 2017), to predict traffic on roads (Li et al., 2017; Cui et al., 2018), to classify and segment images and videos (Wang et al., 2018c; Hu et al., 2017) and 3D meshes and point clouds (Wang et al.,…”
- Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “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], [”
- A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “The only exceptions are [63] and [23], which use road link data with point data as the main datasets.”