Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, such as inter-region traffic, events, and weather. We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the inflow and outflow of crowds in each and every region of 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 residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial…

• MR
  Microsoft Research
• NN
  National Natural Science Foundation of China

  Awards: 61672399, U1401258, 61672399, U1401258
• NK
  National Key Research and Development Program of China

  Awards: 2015CB352400, U1401258