CgNet: Predicting Urban Congregations from Spatio-Temporal Data Using Deep Neural Networks

Predicting urban congregations can help in monitoring a variety of unusual group events, which is of great importance to public safety and traffic management in smart cities. However, it is very challenging because of complicated spatio-temporal correlations. In this article, we propose a deep neural network-based model, entitled CgNet, for urban congregations prediction. Firstly, we design three types of flows to present dependencies between regions among different timestamps to model the mobility of individuals. Secondly, CgNet utilizes four components, including spatial feature extraction, temporal feature extraction, external factors fusion and congregation feature fusion to collaboratively predict congregations. The combination of these components is capable of not only capturing the spatial and temporal correlations simultaneously, but also learning the essential relationships between three flows and congregations in each region at different stages. Finally, we evaluated the effectiveness of CgNet with extensive experimental study on real taxi trajectory data. The results demonstrate the advantages of our model beyond several baselines.