Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks

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.