mlsanityData to 7 Oct 2026

DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

arXiv 2108.09091 ↗First posted 20 Aug 2021cs.LGcited by 73

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Abstract (arXiv)

Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors. By leveraging state-of-the-art deep learning technologies on such data, urban traffic prediction has drawn a lot of attention in AI and Intelligent Transportation System community. The problem can be uniformly modeled with a 3D tensor (T, N, C), where T denotes the total time steps, N denotes the size of the spatial domain (i.e., mesh-grids or graph-nodes), and C denotes the channels of information. According to the specific modeling strategy, the state-of-the-art deep learning models can be divided into three categories: grid-based, graph-based, and multivariate time-series models. In this study, we first synthetically review the deep traffic models as well as the widely used datasets, then build a standard benchmark to comprehensively evaluate their performances with the same settings and metrics. Our study named DL-Traff is implemented with two most popular deep learning frameworks, i.e., TensorFlow and PyTorch, which is already publicly available as two GitHub repositories https://github.com/deepkashiwa20/DL-Traff-Grid and https://github.com/deepkashiwa20/DL-Traff-Graph. With DL-Traff, we hope to deliver a useful resource to researchers who are interested in spatiotemporal data analysis.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 20 Aug 2021.

Builds on

26 of 55 references.

References · 26

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  10. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  11. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
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14 more
  1. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  2. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  3. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  4. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  5. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 164
  6. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  7. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
  8. 2018Temporal Pattern Attention for Multivariate Time Series Forecastingcited by 125
  9. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 119
  10. 2019Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecastingcited by 69
  11. 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Predictioncited by 67
  12. 2019STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecastingcited by 59
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  14. 2020Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Datacited by 23

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 10; 2023: 25; 2024: 23; 2025: 11; 2026: 4.

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  1. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  2. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
  3. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  4. 2023DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Modelcited by 64
  5. 2024ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Modelcited by 48
  6. 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatmentcited by 47
  7. 2022Graph Neural Networks in IoT: A Surveycited by 27
  8. 2023Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecastingcited by 27
  9. 2024GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missingcited by 20
  10. 2023LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Predictioncited by 19
  11. 2023Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecastingcited by 19
  12. 2024Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecastingcited by 18
38 more
  1. 2024Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecastingcited by 18
  2. 2023Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Modelcited by 16
  3. 2022Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecastingcited by 14
  4. 2023Explainable Spatio-Temporal Graph Neural Networkscited by 13
  5. 2023STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecastingcited by 12
  6. 2024Spatio-Temporal Field Neural Networks for Air Quality Inferencecited by 12
  7. 2024Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigmcited by 12
  8. 2022Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecastingcited by 11
  9. 2023Traffic Forecasting on New Roads Using Spatial Contrastive Pre-Training (SCPT)cited by 11
  10. 2023Spatio-Temporal Meta Contrastive Learningcited by 11
  11. 2022ST-ExpertNet: A Deep Expert Framework for Traffic Predictioncited by 10
  12. 2024Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learningcited by 9
  13. 2024STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecastingcited by 9
  14. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  15. 2022Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropoutcited by 7
  16. 2023STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Predictioncited by 7
  17. 2024Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenariocited by 7
  18. 2023Traffic Prediction with Transfer Learning: A Mutual Information-based Approachcited by 6
  19. 2023Learning Gaussian Mixture Representations for Tensor Time Series Forecastingcited by 6
  20. 2023MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptationcited by 6
  21. 2024Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarioscited by 6
  22. 2023Adaptive Modeling of Uncertainties for Traffic Forecastingcited by 5
  23. 2023Message Passing Neural Networks for Traffic Forecastingcited by 4
  24. 2024Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Predictioncited by 4
  25. 2025How Different from the Past? Spatio-Temporal Time Series Forecasting with Self-Supervised Deviation Learningcited by 4
  26. 2022MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modelingcited by 3
  27. 2024Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baselinecited by 3
  28. 2025Towards Unveiling Predictive Uncertainty Vulnerabilities in the Context of the Right to Be Forgottencited by 3
  29. 2022Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decompositioncited by 2
  30. 2023Survey of Federated Learning Models for Spatial-Temporal Mobility Applicationscited by 2
  31. 2023DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecastingcited by 2
  32. 2023Navigating Out-of-Distribution Electricity Load Forecasting during COVID-19: Benchmarking energy load forecasting models without and with continual learningcited by 2
  33. 2024Continuously Evolving Graph Neural Controlled Differential Equations for Traffic Forecastingcited by 2
  34. 2024Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learnedcited by 2
  35. 2025AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networkscited by 2
  36. 2025STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictionscited by 2
  37. 2025Network-Wide Traffic Volume Estimation from Speed Profiles using a Spatio-Temporal Graph Neural Network with Directed Spatial Attentioncited by 2
  38. 2023Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Predictioncited by 1

50 most cited of 73 All 73 (JSON)