Human Flow Prediction Model Based on Graph Convolutional Recurrent Neural Network
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4396-4_1
Reference13 articles.
1. Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 34(1), 914–921 (2020)
2. Fang, X., Huang, J., Wang, F., et al.: Constgat: contextual spatial-temporal graph attention network for travel time estimation at baidu maps. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2697–2705 (2020)
3. Zhang, J., Zheng, Y., Qi, D., et al.: Predicting citywide crowd flows using deep spatiotemporal residual networks. Artif. Intell. 259, 147–166 (2018)
4. Yao, H., Tang, X., Wei, H., et al.: Modeling Spatial-Temporal Dynamics for Traffic Prediction (2018)
5. Shi, C., JinBao, Z., Zhan, G., et al.: Circuit implementation of respiratory information extracted from electrocardiograms. J. Datab. Manage. 33(2) (2022)
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