Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

Understand

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.