A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes

2021/04/21 by Zhengyong Chen, Hongde Wu, Chen, Zhengyong +6 · 2 citations
Computer Science · Engineering · Social Sciences · #Adjacency list #Algorithm #Artificial intelligence #Computer science #Convolutional neural network #Data mining #FOS: Computer and information sciences #FOS: Electrical engineering #Graph #Human Mobility and Location-Based Analysis #Leverage (statistics) #Machine Learning (cs.LG) #Machine learning #Systems and Control (eess.SY) #Theoretical computer science #Traffic Prediction and Management Techniques #Urban Transport and Accessibility #cs.LG #cs.SY #eess.SY #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2104.10644

published in arXiv (Cornell University) (Cornell University) · This manuscript has been accepted at the IEEE ITSC 2021

openalex publication_date 2021/04/21 · arxiv created 2021/07/07 · arxiv updated 2021/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08

Abstract

Accurately forecasting transportation demand is crucial for efficient urban traffic guidance, control and management. One solution to enhance the level of prediction accuracy is to leverage graph convolutional networks (GCN), a neural network based modelling approach with the ability to process data contained in graph based structures. As a powerful extension of GCN, a spatial-temporal graph convolutional network (ST-GCN) aims to capture the relationship of data contained in the graphical nodes across both spatial and temporal dimensions, which presents a novel deep learning paradigm for the analysis of complex time-series data that also involves spatial information as present in transportation use cases. In this paper, we present an Attention-based ST-GCN (AST-GCN) for predicting the number of available bikes in bike-sharing systems in cities, where the attention-based mechanism is introduced to further improve the performance of an ST-GCN. Furthermore, we also discuss the impacts of different modelling methods of adjacency matrices on the proposed architecture. Our experimental results are presented using two real-world datasets, Dublinbikes and NYC-Citi Bike, to illustrate the efficacy of our proposed model which outperforms the majority of existing approaches.

Citations

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• Long Short-Term Memory
• Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
• Coupled Layer-wise Graph Convolution for Transportation Demand Prediction

Cited by

• Machine Learning in Micromobility: A Systematic Review of Datasets, Techniques, and Applications

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