  Being able to predict the crowd flows in each and every part of a city,
especially in irregular regions, is strategically important for traffic
control, risk assessment, and public safety. However, it is very challenging
because of interactions and spatial correlations between different regions. In
addition, it is affected by many factors: i) multiple temporal correlations
among different time intervals: closeness, period, trend; ii) complex external
influential factors: weather, events; iii) meta features: time of the day, day
of the week, and so on. In this paper, we formulate crowd flow forecasting in
irregular regions as a spatio-temporal graph (STG) prediction problem in which
each node represents a region with time-varying flows. By extending graph
convolution to handle the spatial information, we propose using spatial graph
convolution to build a multi-view graph convolutional network (MVGCN) for the
crowd flow forecasting problem, where different views can capture different
factors as mentioned above. We evaluate MVGCN using four real-world datasets
(taxicabs and bikes) and extensive experimental results show that our approach
outperforms the adaptations of state-of-the-art methods. And we have developed
a crowd flow forecasting system for irregular regions that can now be used
internally.
