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ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Research article (2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023) · cited 13× · AI/ML
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks
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ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks is a scholarly article[1].
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ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks. Retrieved May 24, 2026, from https://4ort.xyz/entity/st-gin-an-uncertainty-quantification-approach-in-traffic-data-imputation-with-spatio-temporal-graph-attention-and-bidire
MLA“ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/st-gin-an-uncertainty-quantification-approach-in-traffic-data-imputation-with-spatio-temporal-graph-attention-and-bidire.
BibTeX@misc{4ortxyz_st-gin-an-uncertainty-quantification-approach-in-traffic-data-imputation-with-spatio-temporal-graph-attention-and-bidire_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks}}, year = {2026}, url = {https://4ort.xyz/entity/st-gin-an-uncertainty-quantification-approach-in-traffic-data-imputation-with-spatio-temporal-graph-attention-and-bidire}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks — https://4ort.xyz/entity/st-gin-an-uncertainty-quantification-approach-in-traffic-data-imputation-with-spatio-temporal-graph-attention-and-bidire (retrieved 2026-05-24)
