  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.
