  Forecasting the flow of crowds is of great importance to traffic management
and public safety, yet a very challenging task affected by many complex
factors, such as inter-region traffic, events and weather. In this paper, we
propose a deep-learning-based approach, called ST-ResNet, to collectively
forecast the in-flow and out-flow of crowds in each and every region through a
city. We design an end-to-end structure of ST-ResNet based on unique properties
of spatio-temporal data. More specifically, we employ the framework of the
residual neural networks to model the temporal closeness, period, and trend
properties of the crowd traffic, respectively. For each property, we design a
branch of residual convolutional units, each of which models the spatial
properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the
output of the three residual neural networks based on data, assigning different
weights to different branches and regions. The aggregation is further combined
with external factors, such as weather and day of the week, to predict the
final traffic of crowds in each and every region. We evaluate ST-ResNet based
on two types of crowd flows in Beijing and NYC, finding that its performance
exceeds six well-know methods.
