Shenhao Wang
Orcid: 0000-0003-4374-8193
According to our database1,
Shenhao Wang
authored at least 30 papers
between 2018 and 2025.
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Bibliography
2025
UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction.
CoRR, August, 2025
Graph neural networks for residential location choice: connection to classical logit models.
CoRR, July, 2025
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion.
CoRR, July, 2025
Generative AI for Urban Design: A Stepwise Approach Integrating Human Expertise with Multimodal Diffusion Models.
CoRR, May, 2025
Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models.
CoRR, May, 2025
Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning.
CoRR, March, 2025
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study.
Proceedings of the Companion Proceedings of the ACM on Web Conference 2025, 2025
Proceedings of the Thirteenth International Conference on Learning Representations, 2025
2024
IEEE Trans. Intell. Transp. Syst., October, 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
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network.
CoRR, 2024
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization.
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
2023
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction.
CoRR, 2023
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
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 26th IEEE International Conference on Intelligent Transportation Systems, 2023
2022
Alleviating Data Sparsity Problems in Estimated Time of Arrival via Auxiliary Metric Learning.
IEEE Trans. Intell. Transp. Syst., 2022
Multim. Tools Appl., 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
2021
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models.
CoRR, 2021
CoRR, 2021
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark.
CoRR, 2021
2020
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks.
CoRR, 2020
2019
Deep Neural Networks for Choice Analysis: Architectural Design with Alternative-Specific Utility Functions.
CoRR, 2019
Multitask Learning Deep Neural Network to Combine Revealed and Stated Preference Data.
CoRR, 2019
2018
Using Deep Neural Network to Analyze Travel Mode Choice With Interpretable Economic Information: An Empirical Example.
CoRR, 2018