TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network

Bike-sharing systems have been prevalent since their appearance. As a way to solve the difficulty of the last mile, it can reduce greenhouse gas production. In a bike-sharing system, users can pick up bikes at nearby stations and return them to the stations near their destinations, that provides convenience for users. However, the number of bikes rented from or returned to stations changes over time, causing an imbalance in the number of bikes, and leading to both users? and operators? problems. Bikes flow inequality will lead to inefficient use of bike, and waste users? time when the target station has no dock, or the departure station has no bikes. Bike check-out/in flow prediction is a crucial research and practical issue in bikesharing systems, which plays a vital role in bike rebalancing There are three main research ideas in current studies, and the first is clustering-level flow prediction. i.e., all stations are Nowadays, bike-sharing is available in many cities, solving the problem of the last mile, and it is an environmental-friendly way to commute. However, there is a tidal phenomenon in the bike-sharing system, and the rents/returns of bikes at different stations are unbalanced. Thus, bikes at different stations need to be rebalanced regularly and station-level demand prediction plays an essential role in bike-sharing rebalancing. In this paper, a novel deep graph convolutional network (GCN) model with temporal attention (TAGCN) is proposed for bike check-out/in number prediction of each station. TAGCN can not only model the spatial and temporal dependency between varying stations, but also reflect the influence of different time granularity, which are hour-level, day-level and week-level time periodicity. With the help of well-designed temporal attention mechanism, our model can capture the dynamical temporal correlations and comprehensive spatial patterns in bike check-out/in flow effectively. The proposed model consistently outperforms state-of-the-art methods on four real-world bike-sharing datasets that are four seasons data of Divvy Bike System in Chicago. (c) 2021 Elsevier Inc. All rights reserved.