Multi-Graph Convolutional Recurrent Network for Fine-Grained Lane-Level Traffic Flow Imputation

Author:

Ming Jingci1,Zhang Le2,Fan Wei3,Zhang Weijia4,Mei Yu5,Ling Weicen5,Xiong Hui4

Affiliation:

1. Rutgers University

2. Baidu Research, Baidu Inc

3. University of Central Florida

4. The Hong Kong University of Science and Technology (Guangzhou)

5. Baidu Inc,Department of Intelligent Transportation System

Publisher

IEEE

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. A Brief Review on Missing Traffic Data Imputation Methods for Intelligent Transportation Systems;2024 7th International Symposium on Autonomous Systems (ISAS);2024-05-07

2. Boosting Urban Prediction via Addressing Spatial-Temporal Distribution Shift;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

3. An effective variational auto-encoder-based model for traffic flow imputation;Neural Computing and Applications;2023-11-22

4. TriD-MAE: A Generic Pre-trained Model for Multivariate Time Series with Missing Values;Proceedings of the 32nd ACM International Conference on Information and Knowledge Management;2023-10-21

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