  Urban spatial-temporal flows prediction is of great importance to traffic
management, land use, public safety, etc. Urban flows are affected by several
complex and dynamic factors, such as patterns of human activities, weather,
events and holidays. Datasets evaluated the flows come from various sources in
different domains, e.g. mobile phone data, taxi trajectories data, metro/bus
swiping data, bike-sharing data and so on. To summarize these methodologies of
urban flows prediction, in this paper, we first introduce four main factors
affecting urban flows. Second, in order to further analysis urban flows, a
preparation process of multi-sources spatial-temporal data related with urban
flows is partitioned into three groups. Third, we choose the spatial-temporal
dynamic data as a case study for the urban flows prediction task. Fourth, we
analyze and compare some well-known and state-of-the-art flows prediction
methods in detail, classifying them into five categories: statistics-based,
traditional machine learning-based, deep learning-based, reinforcement
learning-based and transfer learning-based methods. Finally, we give open
challenges of urban flows prediction and an outlook in the future of this
field. This paper will facilitate researchers find suitable methods and open
datasets for addressing urban spatial-temporal flows forecast problems.
