CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting-Reference-Cited by-同舟云学术

CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting

Published:2024-03-04 Issue: Volume:2021 Page:985-993
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Zhou Zhengyang¹^ORCID,Shi Jiahao²^ORCID,Zhang Hongbo²^ORCID,Chen Qiongyu²^ORCID,Wang Xu²^ORCID,Chen Hongyang³^ORCID,Wang Yang²^ORCID

Affiliation:

1. University of Science and Technology of China (USTC) & Suzhou Institute for Advanced Research, USTC, Hefei, China

2. University of Science And Technology of China, Hefei, China

3. Zhejiang Lab, Hangzhou, China

Funder

Key Research Project of Zhejiang Lab

National Natural Science Foundation of China

Academic Leaders Cultivation Program, USTC

Project of Stable Support for Youth Team in Basic Research Field, CAS

National Key Research and Development Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635759

Reference43 articles.

1. T. Li, J. Zhang, K. Bao, Y. Liang, Y. Li, and Y. Zheng, "Autost: Efficient neural architecture search for spatio-temporal prediction," in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 794--802.

2. J. Liu, L. Deng, H. Miao, Y. Zhao, and K. Zheng, "Task assignment with federated preference learning in spatial crowdsourcing," in Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022, pp. 1279--1288.

3. L. Bai, L. Yao, S. S. Kanhere, X.Wang, and Q. Z. Sheng, "Stg2seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting," in 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. International Joint Conferences on Artificial Intelligence, 2019, pp. 1981--1987.

4. L. Han, B. Du, L. Sun, Y. Fu, Y. Lv, and H. Xiong, "Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting," in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 547--555.

5. L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, "Adaptive graph convolutional recurrent network for traffic forecasting," Advances in Neural Information Processing Systems, vol. 33, 2020.

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