RESGCN: RESidual Graph Convolutional Network based Free Dock Prediction in Bike Sharing System

As an environment-friendly public transport, shared bikes have become an important urban transport, providing cheap and convenient services for urban residents. However, the number of docks of a station in a bike sharing system is fixed when it is built, and there exists imbalance between bike usage and supply in reality. An accurate real-time free dock prediction can help guide users to choose a proper station (with free bikes/docks) to rent or return a bike. Many earlier efforts are paid to do bike sharing prediction based on model-based approaches. Recently, deep neural networks (DNN), like convolutional neural networks (CNN) and recurrent neural networks (RNN), have been introduced to solve traffic prediction problems. However, three are some unsolved issues to make accurate real-time free dock prediction, such as learning complicated temporal variation and periodicity of bike usage, spatial correlations of free docks among different stations, and the impact of external factors like weather. To overcome these challenges, we propose a novel deep neural network model, which combines graph convolution and a residual structure together. We first model a bike sharing system as a weighted graph, and the non-Euclidean spatial correlations among stations (represented by weighted edges in the graph) are extracted by random walk operation in graph convolution layers. Moreover, periodic patterns of free docks in different time scales are captured by a residual structure, and external factors are considered to improve the accuracy of prediction. We also conduct comprehensive experiments based on a public real-world dataset of riding trips from Boston, and the results show that our method outperforms state-of-the-art baselines.