# Bike Flow Prediction with Multi-Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1807.10934 · HTML: https://mlsanity.com/paper/1807.10934/

## Key facts

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

## Built on by (most cited first, 30 of 42)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction](https://mlsanity.com/paper/2108.09091/) (2021)
- [Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving](https://mlsanity.com/paper/2109.12764/) (2021)
- [COLA: Cross-city Mobility Transformer for Human Trajectory Simulation](https://mlsanity.com/paper/2403.01801/) (2024)
- [MLPST: MLP is All You Need for Spatio-Temporal Prediction](https://mlsanity.com/paper/2309.13363/) (2023)
- [PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction](https://mlsanity.com/paper/2309.09500/) (2023)
- [Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting](https://mlsanity.com/paper/2305.13036/) (2023)
- [Graph Neural Processes for Spatio-Temporal Extrapolation](https://mlsanity.com/paper/2305.18719/) (2023)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention](https://mlsanity.com/paper/2204.11008/) (2022)
- [Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting](https://mlsanity.com/paper/2309.11284/) (2023)
- [Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis](https://mlsanity.com/paper/2206.15174/) (2022)
- [EPG-MGCN: Ego-Planning Guided Multi-Graph Convolutional Network for Heterogeneous Agent Trajectory Prediction](https://mlsanity.com/paper/2303.17027/) (2023)
- [Improving short-term bike sharing demand forecast through an irregular convolutional neural network](https://mlsanity.com/paper/2202.04376/) (2022)
- [Spatio-temporal neural structural causal models for bike flow prediction](https://mlsanity.com/paper/2301.07843/) (2023)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Graph-Time Convolutional Neural Networks](https://mlsanity.com/paper/2103.01730/) (2021)
- [Deep trip generation with graph neural networks for bike sharing system expansion](https://mlsanity.com/paper/2303.11977/) (2023)
- [Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction](https://mlsanity.com/paper/2101.06954/) (2021)
- [Graph Neural Networks for Traffic Forecasting](https://mlsanity.com/paper/2104.13096/) (2021)
- [Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario](https://mlsanity.com/paper/2410.04740/) (2024)
- [Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow](https://mlsanity.com/paper/2107.13226/) (2021)
- [Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework](https://mlsanity.com/paper/2202.10339/) (2022)
- [Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning](https://mlsanity.com/paper/2212.12767/) (2022)
- [Semantic-Fused Multi-Granularity Cross-City Traffic Prediction](https://mlsanity.com/paper/2302.11774/) (2023)
- [Physics-guided Active Sample Reweighting for Urban Flow Prediction](https://mlsanity.com/paper/2407.13605/) (2024)
- [MAF-GNN: Multi-adaptive Spatiotemporal-flow Graph Neural Network for Traffic Speed Forecasting](https://mlsanity.com/paper/2108.03594/) (2021)
- [Transfer learning for cross-modal demand prediction of bike-share and public transit](https://mlsanity.com/paper/2203.09279/) (2022)
- [A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units](https://mlsanity.com/paper/2403.07022/) (2024)

## 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.