TAGCN: station-level demand prediction for bike-sharing system via a temporal attention graph convolution network
2021
Abstract 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.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
29
References
3
Citations
NaN
KQI