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

• URL: http://arxiv.org/abs/2104.10644v1
• Date: Wed, 21 Apr 2021 17:13:29 GMT
• Title: A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes
• Authors: Zhengyong Chen, Hongde Wu, Noel E. O'Connor, Mingming Liu
• Abstract summary: We present an Attention-based ST-GCN (AST-GCN) for predicting the number of available bikes in bike-sharing systems in cities. Our experimental results are presented using two real-world datasets, Dublinbikes and NYC-Citi Bike.
• Score: 13.819341724635319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 a 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.

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