mlsanityData to 14 Oct 2026

Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach

arXiv 1712.04997 ↗First posted 13 Dec 2017stat.ML cs.LGcited by 35

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Abstract (arXiv)

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations between stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored; GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks, and GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network architecture to capture temporal dependencies in the bike-sharing demand series. Furthermore, four types of GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six types of GCNN models and seven other benchmark models are built and compared on a Citi Bike dataset from New York City which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models. Through a more detailed graph network analysis based on the learned DDGF, insights are obtained on the black box of the GCNN-DDGF model. It is found to capture some information similar to details embedded in the SD, DE and DC matrices. More importantly, it also uncovers hidden heterogeneous pairwise correlations between stations that are not revealed by any of those matrices.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2017.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 7; 2024: 8; 2025: 3; 2026: 1.

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  1. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  2. 2021A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Featurescited by 13
  3. 2022Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Networkcited by 12
  4. 2022Improving short-term bike sharing demand forecast through an irregular convolutional neural networkcited by 9
  5. 2023Fairness-enhancing deep learning for ride-hailing demand predictioncited by 9
  6. 2024GenAI-powered Multi-Agent Paradigm for Smart Urban Mobility: Opportunities and Challenges for Integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) with Intelligent Transportation Systemscited by 9
  7. 2023Deep trip generation with graph neural networks for bike sharing system expansioncited by 8
  8. 2022Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networkscited by 7
  9. 2021Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Managementcited by 6
  10. 2021Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Networkcited by 5
  11. 2021Adversarial Diffusion Attacks on Graph-based Traffic Prediction Modelscited by 5
  12. 2022Short term prediction of demand for ride hailing services: A deep learning approachcited by 4
23 more
  1. 2023XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatilitycited by 3
  2. 2022Transfer learning for cross-modal demand prediction of bike-share and public transitcited by 2
  3. 2022GT-CausIn: a novel causal-based insight for traffic predictioncited by 2
  4. 2023ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobilitycited by 2
  5. 2022Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecastingcited by 1
  6. 2024Development of a graph neural network surrogate for travel demand modellingcited by 1
  7. 2024Predicting travel demand of a bike sharing system using graph convolutional neural networkscited by 1
  8. 2024Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approachcited by 1
  9. 2024Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesiscited by 1
  10. 2025Micromobility Flow Prediction: A Bike Sharing Station-level Study via Multi-level Spatial-Temporal Attention Neural Networkcited by 1
  11. 2026T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobilitycited by 1
  12. 2021Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical contextcited by 0
  13. 2021On the Bike Spreading Problemcited by 0
  14. 2021Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Networkcited by 0
  15. 2022A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systemscited by 0
  16. 2023A Dynamic Temporal Self-attention Graph Convolutional Network for Traffic Predictioncited by 0
  17. 2023Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systemscited by 0
  18. 2023Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNNcited by 0
  19. 2024Improving Demand Forecasting in Open Systems with Cartogram-Enhanced Deep Learningcited by 0
  20. 2024Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing Systemcited by 0
  21. 2024Magazine Supply Optimization: a Case-studycited by 0
  22. 2025Quantum generative model on bicycle-sharing system and an applicationcited by 0
  23. 2025Fluid Grey 2: How Well Does Generative Adversarial Network Learn Deeper Topology Structure in Architecture That Matches Images?cited by 0