  As an economical and healthy mode of shared transportation, Bike Sharing
System (BSS) develops quickly in many big cities. An accurate prediction method
can help BSS schedule resources in advance to meet the demands of users, and
definitely improve operating efficiencies of it. However, most of the existing
methods for similar tasks just utilize spatial or temporal information
independently. Though there are some methods consider both, they only focus on
demand prediction in a single location or between location pairs. In this
paper, we propose a novel deep learning method called Spatial-Temporal Dynamic
Interval Network (STDI-Net). The method predicts the number of renting and
returning orders of multiple connected stations in the near future by modeling
joint spatial-temporal information. Furthermore, we embed an additional module
that generates dynamical learnable mappings for different time intervals, to
include the factor that different time intervals have a strong influence on
demand prediction in BSS. Extensive experiments are conducted on the NYC Bike
dataset, the results demonstrate the superiority of our method over existing
methods.
