2021/04/21 by Zhengyong Chen, Hongde Wu, Chen, Zhengyong +5 · 1 citation
Engineering · Social Sciences · #FOS: Computer and information sciences #FOS: Electrical engineering #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Systems and Control (eess.SY) #Traffic Prediction and Management Techniques #Urban Transport and Accessibility #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2104.10644
openalex publication_date 2021/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.