Dingyi Zhuang
Orcid: 0000-0003-3208-6016
According to our database1,
Dingyi Zhuang
authored at least 25 papers
between 2021 and 2024.
Collaborative distances:
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Bibliography
2024
Uncertainty Quantification of Spatiotemporal Travel Demand With Probabilistic Graph Neural Networks.
IEEE Trans. Intell. Transp. Syst., August, 2024
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Composite Spatial Reasoning.
CoRR, 2024
CoRR, 2024
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network.
CoRR, 2024
Synergizing Spatial Optimization with Large Language Models for Open-Domain Urban Itinerary Planning.
CoRR, 2024
CoRR, 2024
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks.
Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, 2024
ItiNera: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: EMNLP 2024, 2024
2023
IEEE Trans. Intell. Transp. Syst., May, 2023
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction.
CoRR, 2023
Uncertainty Quantification in the Road-level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN).
CoRR, 2023
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction.
CoRR, 2023
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks.
CoRR, 2023
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023
Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper).
Proceedings of the 12th International Conference on Geographic Information Science, 2023
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023
2022
A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting.
IEEE Trans. Intell. Transp. Syst., 2022
Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2022
2021
CoRR, 2021
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021