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

arXiv: https://arxiv.org/abs/1712.04997 · HTML: https://mlsanity.com/paper/1712.04997/

## Key facts

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

## Built on by (most cited first, 30 of 35)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features](https://mlsanity.com/paper/2111.08450/) (2021)
- [Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network](https://mlsanity.com/paper/2202.10508/) (2022)
- [Improving short-term bike sharing demand forecast through an irregular convolutional neural network](https://mlsanity.com/paper/2202.04376/) (2022)
- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [GenAI-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 Systems](https://mlsanity.com/paper/2409.00494/) (2024)
- [Deep trip generation with graph neural networks for bike sharing system expansion](https://mlsanity.com/paper/2303.11977/) (2023)
- [Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networks](https://mlsanity.com/paper/2211.08903/) (2022)
- [Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management](https://mlsanity.com/paper/2108.00858/) (2021)
- [Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network](https://mlsanity.com/paper/2101.07425/) (2021)
- [Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models](https://mlsanity.com/paper/2104.09369/) (2021)
- [Short term prediction of demand for ride hailing services: A deep learning approach](https://mlsanity.com/paper/2212.03956/) (2022)
- [XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility](https://mlsanity.com/paper/2310.09847/) (2023)
- [Transfer learning for cross-modal demand prediction of bike-share and public transit](https://mlsanity.com/paper/2203.09279/) (2022)
- [GT-CausIn: a novel causal-based insight for traffic prediction](https://mlsanity.com/paper/2212.05782/) (2022)
- [ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility](https://mlsanity.com/paper/2306.13897/) (2023)
- [Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting](https://mlsanity.com/paper/2202.10586/) (2022)
- [Development of a graph neural network surrogate for travel demand modelling](https://mlsanity.com/paper/2408.07726/) (2024)
- [Predicting travel demand of a bike sharing system using graph convolutional neural networks](https://mlsanity.com/paper/2408.09317/) (2024)
- [Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach](https://mlsanity.com/paper/2410.08522/) (2024)
- [Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis](https://mlsanity.com/paper/2411.02542/) (2024)
- [Micromobility Flow Prediction: A Bike Sharing Station-level Study via Multi-level Spatial-Temporal Attention Neural Network](https://mlsanity.com/paper/2507.16020/) (2025)
- [T-STAR: A Context-Aware Transformer Framework for Short-Term Probabilistic Demand Forecasting in Dock-Based Shared Micro-Mobility](https://mlsanity.com/paper/2602.06866/) (2026)
- [Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context](https://mlsanity.com/paper/2105.01125/) (2021)
- [On the Bike Spreading Problem](https://mlsanity.com/paper/2107.00761/) (2021)
- [Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network](https://mlsanity.com/paper/2111.11337/) (2021)
- [A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems](https://mlsanity.com/paper/2201.00720/) (2022)
- [A Dynamic Temporal Self-attention Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2302.10428/) (2023)
- [Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems](https://mlsanity.com/paper/2306.01282/) (2023)
- [Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN](https://mlsanity.com/paper/2308.16122/) (2023)

## 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.