3D-ConvLSTMNet: A Deep Spatio-Temporal Model for Traffic Flow Prediction

Lihua He, Wuman Luo

Abstract: Spatiotemporal correlations are crucial for traffic flow prediction. So far, various traffic flow prediction methods based on convolutional neural network (CNN) and long short-term memory (LSTM) network have been proposed. However, the common CNN - based models cannot preserve the temporal information after the first layer. Although the 3D CNN-based models can effectively capture short-term spatial and tempo-ral features, they are not suitable for long-term information capturing. LSTM is excellent at long-term features extraction. However, it alone cannot be used for spatial information extraction. To address these issues, we propose a deep architecture called 3D-ConvLSTMNet to better capture the spatiotemporal correlations among the traffic data. Specifically, we proposed a short-long term spatiotemporal feature extraction module called 3D-ConvLSTM, which uses 3D CNN to extract short-term spatiotemporal correlations, and uses ConvLSTM to extract the long-term spatiotemporal correlations. To get the long-distance spatial features, we adopt the residual neural network to develop the depth of 3D-ConvLSTMNet. Finally, we utilize a channel-wise attention mechanism to quantify the contribution of each grid in space domain. To evaluate the performances of ConvLSTMNet, we conduct extensive experiments on two real-world datasets. The experiment results show that our model gets better performances than the other state-of-the-art methods.