Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

By Weiqiu Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng. AAAI Conference on Artificial Intelligence, 2019

355 citations recorded by the source corpus.

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i.e., the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.

Subfields: Artificial Intelligence, Building and Construction, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/6ca944ae76b4c71c5940c4172ef8222897d6cd51

How researchers see this paper

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

In Artificial Intelligence

1. Shirui Pan
2. Guodong Long
3. Chengqi Zhang
4. Jing Jiang
5. Youfang Lin
6. Shengna Guo
7. Huaiyu Wan
8. Zonghan Wu

In Building and Construction

1. Shirui Pan
2. Shengna Guo
3. Youfang Lin
4. Huaiyu Wan
5. Gao Cong
6. Xiucheng Li
7. Nizar Bouguila
8. Saeed Rahmani

In Environmental Engineering

1. Nizar Bouguila
2. Jing Jiang
3. Guodong Long
4. Saeed Rahmani
5. Asiye Baghbani
6. Zachary Patterson

Citations in the corpus

• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (2020, background): “• MRA-BGCN: A multi-range attentive bicomponent GCN [3].”
• Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution (2021, methodology): “CNN [1]–[4], GNN [5]–[8], [12]–[23] and attention mechanism [7]–[11], [17], [19], [23], [25] have been widely used to capture spatial dependency.”
• Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (2021, methodology): “Related variants have shown success in time-dependent problems: for example, GNN-based models can perform well in traffic prediction tasks (Yu, Yin, and Zhu 2017; Chen et al. 2019).”
• Graph Neural Network for Traffic Forecasting: A Survey (2021, neutral): “Dataset Name Relevant Studies METR-LA [3], [4], [22], [25], [26], [27], [35], [38], [43], [44], [55], [57], [71], [72], [80], [105], [110], [117], [125], [129], [132], [137], [138], [141], [142], [143], [144], [145], [146], [148], [149], [153], [155], [159], [160], [172] PeMS all [28], [147] PeMS-BA”
• Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, background): “GMAN, and MRA-BGCN [42]) forecasting, and reported the”
• Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021, methodology): “To model the spatial correlations, the de facto strategy is to perform the graph convolution over the graph induced by the road networks [6], [7], [8], [9], [12], whose structure is static.”