ST-GIN: An Uncertainty Quantification Approach in Traffic Data

Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from loop detectors or similar sources, often contains missing values (MV), which can adversely impact associated applications and research.

Data and Resources

Cite this as

Zepu Wang, Dingyi Zhuang, Yankai Li, Jinhua Zhao, Peng Sun, Shenhao Wang, Yulin Hu (2024). Dataset: ST-GIN: An Uncertainty Quantification Approach in Traffic Data. https://doi.org/10.57702/25ofgng7

DOI retrieved: December 3, 2024

Additional Info

Field Value
Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.2305.06480
Author Zepu Wang
More Authors
Dingyi Zhuang
Yankai Li
Jinhua Zhao
Peng Sun
Shenhao Wang
Yulin Hu