ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks-Reference-Cited by-同舟云学术

ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks

Published:2023-09-24 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
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Author:

Wang Zepu¹,Zhuang Dingyi²,Li Yankai¹,Zhao Jinhua²,Sun Peng³,Wang Shenhao⁴,Hu Yulin⁵

Affiliation:

1. University of Pennsylvania,Department of Computer and Information Science

2. Massachusetts Institute of Technology,Department of Civil and Environmental Engineering

3. Duke Kunshan University,Department of Natural and Applied Sciences

4. University of Florida,Department of Urban and Regional Planning

5. Wuhan University,Department of Information and Communication Engineering

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10420842/10420843/10422526.pdf?arnumber=10422526

Reference31 articles.

1. A Novel Time Efficient Machine Learning-based Traffic Flow Prediction Method for Large Scale Road Network

2. Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications

3. Exploring Imputation Techniques for Missing Data in Transportation Management Systems

4. New Algorithms for Filtering and Imputation of Real-Time and Archived Dual-Loop Detector Data in I-4 Data Warehouse

5. Markov Chain Monte Carlo Multiple Imputation Using Bayesian Networks for Incomplete Intelligent Transportation Systems Data

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