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Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks |
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Massachusetts Institute of Technology, Jun. 2021 ~ Feb. 2022
Advisor: Prof. Shenhao Wang, Prof. Haris N Koutsopoulos, Prof. Jinhua Zhao
Publication: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Resources: KDD (Video included) ar5iv GitHub DOI
- Designed a Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) to quantify the uncertainty of the sparse travel demand.
- Analyzed spatial and temporal correlations using diffusion and temporal convolution networks, which are then fused to parameterize the probabilistic distributions of travel demand.
- The results demonstrate the superiority of STZINB-GNN over benchmark models, especially under high spatial-temporal resolutions, because of its high accuracy, tight confidence intervals, and interpretable parameters.
