Funder
National Natural Science Foundation of China
Subject
Artificial Intelligence,Computer Science Applications,General Engineering
Reference48 articles.
1. Bai, L., Yao, L., Kanhere, S. S., Wang, X., Liu, W., & Yang, Z. (2019). Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 2293–2296).
2. STG2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting;Bai,2019
3. Spatial-temporal complex graph convolution network for traffic flow prediction;Bao;Engineering Applications of Artificial Intelligence,2023
4. Exploiting spatio-temporal correlations with multiple 3D convolutional neural networks for citywide vehicle flow prediction;Chen,2018
5. Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks;Chen;ACM Transactions on Knowledge Discovery from Data,2020
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献