  Nowadays, massive urban human mobility data are being generated from mobile
phones, car navigation systems, and traffic sensors. Predicting the density and
flow of the crowd or traffic at a citywide level becomes possible by using the
big data and cutting-edge AI technologies. It has been a very significant
research topic with high social impact, which can be widely applied to
emergency management, traffic regulation, and urban planning. In particular, by
meshing a large urban area to a number of fine-grained mesh-grids, citywide
crowd and traffic information in a continuous time period can be represented
like a video, where each timestamp can be seen as one video frame. Based on
this idea, a series of methods have been proposed to address video-like
prediction for citywide crowd and traffic. In this study, we publish a new
aggregated human mobility dataset generated from a real-world smartphone
application and build a standard benchmark for such kind of video-like urban
computing with this new dataset and the existing open datasets. We first
comprehensively review the state-of-the-art works of literature and formulate
the density and in-out flow prediction problem, then conduct a thorough
performance assessment for those methods. With this benchmark, we hope
researchers can easily follow up and quickly launch a new solution on this
topic.
