mlsanityData to 7 Oct 2026

Dynamic Causal Graph Convolutional Network for Traffic Prediction

arXiv 2306.07019 ↗First posted 12 Jun 2023cs.LG eess.SPcited by 12

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Abstract (arXiv)

Modeling complex spatiotemporal dependencies in correlated traffic series is essential for traffic prediction. While recent works have shown improved prediction performance by using neural networks to extract spatiotemporal correlations, their effectiveness depends on the quality of the graph structures used to represent the spatial topology of the traffic network. In this work, we propose a novel approach for traffic prediction that embeds time-varying dynamic Bayesian network to capture the fine spatiotemporal topology of traffic data. We then use graph convolutional networks to generate traffic forecasts. To enable our method to efficiently model nonlinear traffic propagation patterns, we develop a deep learning-based module as a hyper-network to generate stepwise dynamic causal graphs. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method. The code is available at https://github.com/MonBG/DCGCN.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 19 of its 45 references that are in the map. First posted on arXiv 12 Jun 2023.

Builds on

19 of 45 references.

References · 19

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  7. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  8. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  9. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  10. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  11. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  12. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
7 more
  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  3. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 156
  4. 2022Jointly Contrastive Representation Learning on Road Network and Trajectorycited by 34
  5. 2019Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Predictioncited by 30
  6. 2020Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profilecited by 16
  7. 2023MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysiscited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 1; 2026: 1.

12 papers.

  1. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  2. 2023Adaptive Hierarchical SpatioTemporal Network for Traffic Forecastingcited by 5
  3. 2024Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learningcited by 5
  4. 2023Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clusteringcited by 4
  5. 2023VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Networkcited by 3
  6. 2024TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidentscited by 3
  7. 2024Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Marketcited by 1
  8. 2025Data-Driven Optimization of EV Charging Station Placement Using Causal Discoverycited by 1
  9. 2023Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learningcited by 0
  10. 2024Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariatescited by 0
  11. 2024HydroVision: LiDAR-Guided Hydrometric Prediction with Vision Transformers and Hybrid Graph Learningcited by 0
  12. 2026CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effectscited by 0