mlsanityData to 14 Oct 2026

Bike Flow Prediction with Multi-Graph Convolutional Networks

arXiv 1807.10934 ↗First posted 28 Jul 2018cs.LG cs.AI stat.MLcited by 42

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Abstract (arXiv)

One fundamental issue in managing bike sharing systems is the bike flow prediction. Due to the hardness of predicting the flow for a single station, recent research works often predict the bike flow at cluster-level. While such studies gain satisfactory prediction accuracy, they cannot directly guide some fine-grained bike sharing system management issues at station-level. In this paper, we revisit the problem of the station-level bike flow prediction, aiming to boost the prediction accuracy leveraging the breakthroughs of deep learning techniques. We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective. More specifically, we construct multiple inter-station graphs for a bike sharing system. In each graph, nodes are stations, and edges are a certain type of relations between stations. Then, multiple graphs are constructed to reflect heterogeneous relationships (e.g., distance, ride record correlation). Afterward, we fuse the multiple graphs and then apply the convolutional layers on the fused graph to predict station-level future bike flow. In addition to the estimated bike flow value, our model also gives the prediction confidence interval so as to help the bike sharing system managers make decisions. Using New York City and Chicago bike sharing data for experiments, our model can outperform state-of-the-art station-level prediction models by reducing 25.1% and 17.0% of prediction error in New York City and Chicago, respectively.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jul 2018.

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Citing papers per year: 2021: 10; 2022: 6; 2023: 14; 2024: 7; 2025: 4; 2026: 1.

42 papers.

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  1. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  2. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  3. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  4. 2021Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Drivingcited by 36
  5. 2024COLA: Cross-city Mobility Transformer for Human Trajectory Simulationcited by 25
  6. 2023MLPST: MLP is All You Need for Spatio-Temporal Predictioncited by 22
  7. 2023PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Predictioncited by 20
  8. 2023Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecastingcited by 19
  9. 2023Graph Neural Processes for Spatio-Temporal Extrapolationcited by 19
  10. 2023Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunitiescited by 16
  11. 2022Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attentioncited by 15
  12. 2023Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecastingcited by 11
30 more
  1. 2022Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysiscited by 10
  2. 2023EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Predictioncited by 10
  3. 2022Improving short-term bike sharing demand forecast through an irregular convolutional neural networkcited by 9
  4. 2023Spatio-temporal neural structural causal models for bike flow predictioncited by 9
  5. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  6. 2021Graph-Time Convolutional Neural Networkscited by 8
  7. 2023Deep trip generation with graph neural networks for bike sharing system expansioncited by 8
  8. 2021Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Predictioncited by 7
  9. 2021Graph Neural Networks for Traffic Forecastingcited by 7
  10. 2024Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenariocited by 7
  11. 2021Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flowcited by 5
  12. 2022Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Frameworkcited by 5
  13. 2022Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learningcited by 4
  14. 2023Semantic-Fused Multi-Granularity Cross-City Traffic Predictioncited by 4
  15. 2024Physics-guided Active Sample Reweighting for Urban Flow Predictioncited by 3
  16. 2021MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecastingcited by 2
  17. 2022Transfer learning for cross-modal demand prediction of bike-share and public transitcited by 2
  18. 2024A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Unitscited by 2
  19. 2025GraphSparseNet: a Novel Method for Large Scale Traffic Flow Predictioncited by 2
  20. 2021Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmarkcited by 1
  21. 2024Predicting travel demand of a bike sharing system using graph convolutional neural networkscited by 1
  22. 2024Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditionscited by 1
  23. 2025STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Modelscited by 1
  24. 2025A Multi-Layer CNN-GRUSKIP model based on transformer for spatial TEMPORAL traffic flow predictioncited by 1
  25. 2021Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequencycited by 0
  26. 2023Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systemscited by 0
  27. 2023UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Servicescited by 0
  28. 2023Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Predictioncited by 0
  29. 2025Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharingcited by 0
  30. 2026Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)cited by 0