Abstract
Crowd flow prediction is of great significance to the construction of smart cities, and recently became a research hot-spot. As road conditions are constantly changing, the forecasting crowd flows accurately and efficiently is a challenging task. One of the key factors to accomplish this prediction task is how to temporally and spatially model the evolution trend of crowd flows. In previous works, capturing features is carried out mainly by utilizing the structure based on a recurrent neural network which is effective to capture temporal features from time sequence. However, it is inefficient for capturing spatial-temporal features which is critical for the prediction task. In this paper, we develop an elementary module, a 3D convolution layer based on the self-attention mechanism (3DAM), which can extract spatial features and temporal correlation simultaneously. Our proposed spatial-temporal attention 3D convolution prediction network (STA3DCNN) is composed of 3DAMs. Finally, we conduct comparative and self-studying experiments to evaluate the performance of our model on two benchmark datasets. The experimental results demonstrate that the proposed model performs effectively, and outperforms 9 representative methods.
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Acknowledgment
This research was supported by National Key R&D Program of China (No. 2017YFC0806000), National Natural Science Foundation of China (No. 11627802, 51678249, 61871188), State Key Lab of Subtropical Building Science, South China University of Technology (2018ZB33), and the State Scholar- ship Fund of China Scholarship Council (201806155022).
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Tang, G., Zhou, Z., Li, B. (2021). STA3DCNN: Spatial-Temporal Attention 3D Convolutional Neural Network for Citywide Crowd Flow Prediction. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_29
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