Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework

Understand

Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks.

• By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks.
• And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can successfully characterize the temporal correlation, thus exhibits superior capability for time series tasks.
• Traffic flow data have plentiful characteristics on both time and space domain.