mlsanityData to 13 Oct 2026

Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

arXiv 1903.07789 ↗First posted 19 Mar 2019cs.CV cs.LGcited by 40

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Abstract (arXiv)

Being able to predict the crowd flows in each and every part of a city, especially in irregular regions, is strategically important for traffic control, risk assessment, and public safety. However, it is very challenging because of interactions and spatial correlations between different regions. In addition, it is affected by many factors: i) multiple temporal correlations among different time intervals: closeness, period, trend; ii) complex external influential factors: weather, events; iii) meta features: time of the day, day of the week, and so on. In this paper, we formulate crowd flow forecasting in irregular regions as a spatio-temporal graph (STG) prediction problem in which each node represents a region with time-varying flows. By extending graph convolution to handle the spatial information, we propose using spatial graph convolution to build a multi-view graph convolutional network (MVGCN) for the crowd flow forecasting problem, where different views can capture different factors as mentioned above. We evaluate MVGCN using four real-world datasets (taxicabs and bikes) and extensive experimental results show that our approach outperforms the adaptations of state-of-the-art methods. And we have developed a crowd flow forecasting system for irregular regions that can now be used internally.

Key facts. Cited by 40 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Mar 2019.

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Citing papers per year: 2021: 13; 2022: 5; 2023: 7; 2024: 13; 2025: 2.

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  1. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  4. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  5. 2024Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlookcited by 33
  6. 2022Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networkscited by 29
  7. 2021Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecastingcited by 23
  8. 2024Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Eracited by 23
  9. 2021A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecastingcited by 21
  10. 2023Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Modelcited by 17
  11. 2021Decoupling Long- and Short-Term Patterns in Spatiotemporal Inferencecited by 15
  12. 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysiscited by 13
28 more
  1. 2022Deep reinforcement learning guided graph neural networks for brain network analysiscited by 12
  2. 2023Urban Regional Function Guided Traffic Flow Predictioncited by 12
  3. 2024Spatio-Temporal Field Neural Networks for Air Quality Inferencecited by 12
  4. 2021Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregationcited by 11
  5. 2024A Universal Model for Human Mobility Predictioncited by 10
  6. 2023Spatio-temporal neural structural causal models for bike flow predictioncited by 9
  7. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  8. 2024Pretrained Mobility Transformer: A Foundation Model for Human Mobilitycited by 8
  9. 2021Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Predictioncited by 7
  10. 2021Graph Neural Networks for Traffic Forecastingcited by 7
  11. 2024Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenariocited by 7
  12. 2021Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flowcited by 4
  13. 2021Road Network Guided Fine-Grained Urban Traffic Flow Inferencecited by 4
  14. 2024RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregationcited by 4
  15. 2025AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networkscited by 2
  16. 2021Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmarkcited by 1
  17. 2021Periodic Residual Learning for Crowd Flow Forecastingcited by 1
  18. 2022Multi-view Graph Convolutional Networks with Differentiable Node Selectioncited by 1
  19. 2023Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inferencecited by 1
  20. 2023CROSS-Net: Region-Agnostic Taxi-Demand Prediction Using Feature Disentanglementcited by 1
  21. 2024Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensorscited by 1
  22. 2025STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Modelscited by 1
  23. 2021Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecastingcited by 0
  24. 2022STG-GAN: A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systemscited by 0
  25. 2022Attention-based Contextual Multi-View Graph Convolutional Networks for Short-term Population Predictioncited by 0
  26. 2024A Hierarchical Framework with Spatio-Temporal Consistency Learning for Emergence Detection in Complex Adaptive Systemscited by 0
  27. 2024Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectivescited by 0
  28. 2024Self-Supervised State Space Model for Real-Time Traffic Accident Prediction Using eKAN Networkscited by 0