mlsanityData to 14 Oct 2026

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

arXiv 1707.01926 ↗First posted 6 Jul 2017cs.LG stat.MLcited by 41

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Abstract (arXiv)

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jul 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 0; 2024: 21; 2025: 5; 2026: 6.

41 papers.

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  1. 2021Granger Causality: A Review and Recent Advancescited by 63
  2. 2024UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Modelscited by 13
  3. 2024REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updatescited by 12
  4. 2024A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directionscited by 10
  5. 2024SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecastingcited by 8
  6. 2022A Graph-based U-Net Model for Predicting Traffic in unseen Citiescited by 4
  7. 2024Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecastingcited by 4
  8. 2024RDPI: A Refine Diffusion Probability Generation Method for Spatiotemporal Data Imputationcited by 4
  9. 2024Counterfactual Explanations for Deep Learning-Based Traffic Forecastingcited by 3
  10. 2024Cross Space and Time: A Spatio-Temporal Unitized Model for Traffic Flow Forecastingcited by 3
  11. 2024Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scalescited by 3
  12. 2025A Modular Multitask Reasoning Framework Integrating Spatio-temporal Models and LLMscited by 3
29 more
  1. 2021MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecastingcited by 2
  2. 2021Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility informationcited by 2
  3. 2022Granger Causality using Neural Networkscited by 2
  4. 2022Application of Graph Neural Networks and graph descriptors for graph classificationcited by 2
  5. 2024Distribution-aware Online Continual Learning for Urban Spatio-Temporal Forecastingcited by 1
  6. 2024STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal De-Confounding Strategy for Crowd Flow Predictioncited by 1
  7. 2026Making AI Scientists Auditable from Evidence to Claimcited by 1
  8. 2022Topics in Deep Learning and Optimization Algorithms for IoT Applications in Smart Transportationcited by 0
  9. 2022M3FGM:a node masking and multi-granularity message passing-based federated graph model for spatial-temporal data predictioncited by 0
  10. 2022Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecastingcited by 0
  11. 2024LightningNet: Distributed Graph-based Cellular Network Performance Forecasting for the Edgecited by 0
  12. 2024NeuroKoopman Dynamic Causal Discoverycited by 0
  13. 2024Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamicscited by 0
  14. 2024TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecastingcited by 0
  15. 2024Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Predictioncited by 0
  16. 2024Multi-Knowledge Fusion Network for Time Series Representation Learningcited by 0
  17. 2024Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networkscited by 0
  18. 2024Information propagation dynamics in Deep Graph Networkscited by 0
  19. 2024Deep Learning-driven Mobile Traffic Measurement Collection and Analysiscited by 0
  20. 2024Multi-View Fusion Neural Network for Traffic Demand Predictioncited by 0
  21. 2025Hierarchical Information-Guided Spatio-Temporal Mamba for Stock Time Series Forecastingcited by 0
  22. 2025TGDT: A Temporal Graph-based Digital Twin for Urban Traffic Corridorscited by 0
  23. 2025STRATA-TS: Selective Knowledge Transfer for Urban Time Series Forecasting with Retrieval-Guided Reasoningcited by 0
  24. 2025PruneGCRN: Minimizing and explaining spatio-temporal problems through node pruningcited by 0
  25. 2026Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learningcited by 0
  26. 2026Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Frameworkcited by 0
  27. 2026Selective Ambulance Dispatch Under Contextual Travel-Time Uncertaintycited by 0
  28. 2026Beyond the Graph: An Adaptive Meta-Learner Fuses Explainability, Weather, and Dynamics for Robust Bus ETA Predictioncited by 0
  29. 2026Geometry-Aware Operator Families for Structured Representation Learningcited by 0