UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow
  Prediction
Liyue Chen Di Chai Leye Wang
Spatiotemporal crowd flow prediction is one of the key technologies in smart
cities. Currently, there are two major pain points that plague related research
and practitioners. Firstly, crowd flow is related to multiple domain knowledge
factors; however, due to the diversity of application scenarios, it is
difficult for subsequent work to make reasonable and comprehensive use of
domain knowledge. Secondly, with the development of deep learning technology,
the implementation of relevant techniques has become increasingly complex;
reproducing advanced models has become a time-consuming and increasingly
cumbersome task. To address these issues, we design and implement a
spatiotemporal crowd flow prediction toolbox called UCTB (Urban Computing Tool
Box), which integrates multiple spatiotemporal domain knowledge and
state-of-the-art models simultaneously. The relevant code and supporting
documents have been open-sourced at https://github.com/uctb/UCTB.