Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction-Reference-Cited by-同舟云学术

Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction

Published:2023-10-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
language:
Short-container-title:

Author:

Jiang Xinke¹^ORCID,Zhuang Dingyi²^ORCID,Zhang Xianghui³^ORCID,Chen Hao⁴^ORCID,Luo Jiayuan⁵^ORCID,Gao Xiaowei³^ORCID

Affiliation:

1. Peking University, Beijing, China

2. Massachusetts Institute of Technology, Cambridge, MA, USA

3. University College London, London, United Kingdom

4. University of Chinese Academy of Sciences Beijing, Beijing, China

5. Chengdu University of Technology, Chengdu, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3615215

Reference36 articles.

1. Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic

2. Series evaluation of Tweedie exponential dispersion model densities

3. Yuchen Fang , Yanjun Qin , Haiyong Luo , Fang Zhao , Bingbing Xu , Liang Zeng , and Chenxing Wang . 2023. When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) . IEEE , 517--529. Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, and Chenxing Wang. 2023. When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 517--529.

4. Yuchen Fang , Yanjun Qin , Haiyong Luo , Fang Zhao , Liang Zeng , Bo Hui , and Chenxing Wang . 2021 . Cdgnet: A cross-time dynamic graph-based deep learning model for traffic forecasting. arXiv preprint arXiv:2112.02736 (2021). Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, and Chenxing Wang. 2021. Cdgnet: A cross-time dynamic graph-based deep learning model for traffic forecasting. arXiv preprint arXiv:2112.02736 (2021).

5. Learning All Dynamics: Traffic Forecasting via Locality-Aware Spatio-Temporal Joint Transformer

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

1. Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks;Sustainable Cities and Society;2024-11

2. ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24

3. Incomplete Graph Learning via Attribute-Structure Decoupled Variational Auto-Encoder;Proceedings of the 17th ACM International Conference on Web Search and Data Mining;2024-03-04

4. ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks;2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC);2023-09-24