Shenhao Wang

Orcid: 0000-0003-4374-8193

According to our database1, Shenhao Wang authored at least 20 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

On csauthors.net:

Bibliography

2024
Robust Transit Frequency Setting Problem With Demand Uncertainty.
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

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks.
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

2023
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction.
CoRR, 2023

Fairness-enhancing deep learning for ride-hailing demand 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 25th 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

End-to-end video compression for surveillance and conference videos.
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

Estimating air quality co-benefits of energy transition using machine learning.
CoRR, 2021

Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark.
CoRR, 2021

Choice modelling in the age of machine learning.
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


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