Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction.
Lei Bai|Lina Yao|Salil S. Kanhere|Xianzhi Wang|Wei Liu|Zheng Yang
| Anthology ID: | DBLP:conf/cikm/BaiYK0LY19 |
|---|---|
| Volume: | Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019. |
| Year: | 2019 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 2293-2296 |
| URL: | https://doi.org/10.1145/3357384.3358097 |
| DOI: | https://doi.org/10.1145/3357384.3358097 |
| DBLP: | conf/cikm/BaiYK0LY19 |
| BibTeX: |
@inproceedings{bai-2019-spatiotemporal,
author = {Lei Bai and
Lina Yao and
Salil S. Kanhere and
Xianzhi Wang and
Wei Liu and
Zheng Yang},
editor = {Wenwu Zhu and
Dacheng Tao and
Xueqi Cheng and
Peng Cui and
Elke A. Rundensteiner and
David Carmel and
Qi He and
Jeffrey Xu Yu},
title = {{Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction}},
booktitle = {{Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019}},
pages = {2293--2296},
publisher = {ACM},
year = {2019},
url = {https://doi.org/10.1145/3357384.3358097},
doi = {https://doi.org/10.1145/3357384.3358097}
}