  Bike-sharing systems (BSSs) have become increasingly popular around the globe
and have attracted a wide range of research interests. In this paper, the
demand forecasting problem in BSSs is studied. Spatial and temporal features
are critical for demand forecasting in BSSs, but it is challenging to extract
spatiotemporal dynamics. Another challenge is to capture the relations between
spatiotemporal dynamics and external factors, such as weather, day-of-week, and
time-of-day. To address these challenges, we propose a multiple spatiotemporal
fusion network named MSTF-Net. MSTF-Net consists of multiple spatiotemporal
blocks: 3D convolutional network (3D-CNN) blocks, eidetic 3D convolutional long
short-term memory networks (E3D-LSTM) blocks, and fully-connected (FC) blocks.
Specifically, 3D-CNN blocks highlight extracting short-term spatiotemporal
dependence in each fragment (i.e., closeness, period, and trend); E3D-LSTM
blocks further extract long-term spatiotemporal dependence over all fragments;
FC blocks extract nonlinear correlations of external factors. Finally, the
latent representations of E3D-LSTM and FC blocks are fused to obtain the final
prediction. For two real-world datasets, it is shown that MSTF-Net outperforms
seven state-of-the-art models.
