STANN: A Spatio-Temporal Attentive Neural Network for Traffic Prediction

Recently, traffic prediction based on deep learning methods has attracted much attention. However, there still exist two major challenges, namely, dynamic spatio-temporal dependences among network-wide links and long-term traffic prediction for the next few hours. To address these two challenges, this paper proposes a spatio-temporal attentive neural network (STANN) for the network-wide and long-term traffic prediction. The STANN captures the spatial-temporal dependences based on the encoder-decoder architecture with the attention mechanisms. In the encoder, the STANN learns the spatio-temporal dependences from historical traffic series using a recurrent neural network (RNN) with long short-term memory (LSTM) units, in which a new spatial attention model is developed to consider the contribution of each link to the network-wide prediction. In the decoder, the STANN exploits another RNN with LSTM units and a temporal attention model to select the relevant and important historical spatio-temporal dependences from the encoder for long-term traffic prediction. Finally, we conduct extensive experiments to evaluate STANN on three real-world traffic datasets. The experimental results show that the STANN is significantly better than other state-of-the-art models.