mlsanityData to 14 Oct 2026

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

arXiv 1911.12093 ↗First posted 27 Nov 2019cs.LG stat.MLcited by 69

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Abstract (arXiv)

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.

Key facts. Cited by 69 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 17; 2022: 15; 2023: 22; 2024: 10; 2025: 4; 2026: 1.

69 papers.

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  1. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  2. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  3. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157
  4. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  5. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  6. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  7. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  8. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  9. 2021AutoCTS: Automated Correlated Time Series Forecasting -- Extended Versioncited by 48
  10. 2022Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecastingcited by 42
  11. 2021Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecastingcited by 23
  12. 2022Multivariate Time Series Forecasting with Latent Graph Inferencecited by 20
38 more
  1. 2024LightTR: A Lightweight Framework for Federated Trajectory Recoverycited by 19
  2. 2023LightCTS: A Lightweight Framework for Correlated Time Series Forecastingcited by 18
  3. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
  4. 2023Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunitiescited by 16
  5. 2024Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learningcited by 15
  6. 2022Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecastingcited by 14
  7. 2023MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimizationcited by 13
  8. 2022STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graphcited by 12
  9. 2023Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecastingcited by 11
  10. 2024AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecastingcited by 10
  11. 2021Scale-Aware Neural Architecture Search for Multivariate Time Series Forecastingcited by 9
  12. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  13. 2023STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Predictioncited by 8
  14. 2021DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Predictioncited by 6
  15. 2023Learning Gaussian Mixture Representations for Tensor Time Series Forecastingcited by 6
  16. 2022Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-seriescited by 5
  17. 2022Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commercecited by 5
  18. 2023Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approachcited by 5
  19. 2023TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networkscited by 5
  20. 2025GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chainingcited by 5
  21. 2021An Empirical Experiment on Deep Learning Models for Predicting Traffic Datacited by 4
  22. 2021Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimationcited by 4
  23. 2021Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecastingcited by 4
  24. 2021DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecastingcited by 3
  25. 2023Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Frameworkcited by 3
  26. 2021MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecastingcited by 2
  27. 2021Parallel Multi-Graph Convolution Network For Metro Passenger Volume Predictioncited by 2
  28. 2023Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspectivecited by 2
  29. 2023DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecastingcited by 2
  30. 2023Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]cited by 2
  31. 2023TPRNN: A Top-Down Pyramidal Recurrent Neural Network for Time Series Forecastingcited by 2
  32. 2024A novel hybrid time-varying graph neural network for traffic flow forecastingcited by 2
  33. 2024Continuously Evolving Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 2
  34. 2024Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecastingcited by 2
  35. 2026FedDis: A Causal Disentanglement Framework for Federated Traffic Predictioncited by 2
  36. 2022Balanced Graph Structure Learning for Multivariate Time Series Forecastingcited by 1
  37. 2022Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecastingcited by 1
  38. 2022Spatio-Temporal-Frequency Graph Attention Convolutional Network for Aircraft Recognition Based on Heterogeneous Radar Networkcited by 1

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