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Shenhao Wang

University of Florida (from arXiv:2102.01130, 2021) · ORCID · OpenAlex

17 papers in scope · 16 published · 2 on the econ.EM arXiv · 674 citations · h-index 12 (over the papers listed here)

Related authors

The 20 authors closest to this one in our weighted citation graph, most related first.

  1. Daniel F. Villarraga
  2. Ricardo A. Daziano
  3. Easton Huch
  4. Michael Keane
  5. Connor R. Forsythe
  6. Cristian Arteaga
  7. John Paul Helveston
  8. Francisco C. Pereira
  9. Carlos Lima Azevedo
  10. Ioanna Arkoudi
  11. Jinhua Zhao
  12. Baichuan Mo
  13. Bilal Farooq
  14. Melvin Wong
  15. Akshay Vij
  16. Stephane Hess
  17. Zhuodong Tang
  18. Xiaocheng Li
  19. Hanzhao Wang
  20. Kalyan Talluri

Proximity is measured over citations between two papers we both hold, weighted by how heavily one leans on the other, and is symmetric — it does not distinguish citing from being cited. Authors without a profile here are skipped, and a genuinely close colleague can be missing simply because their work is not in our arXiv corpus. Method: docs/06-citations-pipeline.md.

Papers

(2 of 17)

Graph neural networks for residential location choice: Connection to classical logit models
published2026 · Transportation Research Part B Methodological · 1 citations
with Zhanhong Cheng, Lingqian Hu, Yuheng Bu, Yuqi Zhou
Transportation Research Part B Methodological
Comparing hundreds of machine learning and discrete choice models for travel demand modeling: An empirical benchmark
published2024 · Transportation Research Part B Methodological · 25 citations
with Baichuan Mo, Yunhan Zheng, Stephane Hess, Jinhua Zhao
Transportation Research Part B Methodological
Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization
published2024 · Transportation Research Part C Emerging Technologies · 13 citations
with Siqi Feng, Rui Yao, Stephane Hess, Ricardo A. Daziano, Timothy Brathwaite, Joan L. Walker
Transportation Research Part C Emerging Technologies
Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?
published2023 · Transportation Research Part B Methodological · 12 citations
with Qingyi Wang, Yunhan Zheng, Hongzhou Lin, Xiaohu Zhang, Jinhua Zhao, Joan L. Walker
Transportation Research Part B Methodological
Cooperative bus holding and stop-skipping: A deep reinforcement learning framework
published2023 · Transportation Research Part C Emerging Technologies · 25 citations · first circulated 2022
with Joseph Rodriguez, Haris N. Koutsopoulos, Jinhua Zhao
Transportation Research Part C Emerging Technologies
Comparing modelling approaches for the estimation of government intervention effects in COVID-19: Impact of voluntary behavior changes
published2023 · PLoS ONE · 7 citations
with Lun Liu, Zhu Zhang, Hui Wang, Shengsheng Zhuang, Jishan Duan
PLoS ONE
Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks
published2022 · Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · 49 citations
with Dingyi Zhuang, Haris N. Koutsopoulos, Jinhua Zhao
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models
published2021 · Transportation Research Part C Emerging Technologies · 42 citations
with Yunhan Zheng, Jinhua Zhao
Transportation Research Part C Emerging Technologies
Deep neural networks for choice analysis: A statistical learning theory perspective
published2021 · Transportation Research Part B Methodological · 8 citations · first circulated 2018
with Qingyi Wang, Nate Bailey, Jinhua Zhao
Transportation Research Part B Methodological
Measuring policy leakage of Beijing’s car ownership restriction
published2021 · Transportation Research Part A Policy and Practice · 31 citations
with Yunhan Zheng, Joanna Moody, Jinhua Zhao
Transportation Research Part A Policy and Practice
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks
published2021 · Transportation Research Part B Methodological · 55 citations · first circulated 2020
with Baichuan Mo, Jinhua Zhao
Paper profile Transportation Research Part B Methodological arXiv:2010.11644
Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark
working paper2021 · arXiv · 21 citations
with Baichuan Mo, Yunhan Zheng, Stephane Hess, Jinhua Zhao
Paper profile arXiv arXiv:2102.01130
Multitask learning deep neural networks to combine revealed and stated preference data
published2020 · Journal of Choice Modelling · 2 citations · first circulated 2019
with Qingyi Wang, Jinhua Zhao
Journal of Choice Modelling
Deep neural networks for choice analysis: Extracting complete economic information for interpretation
published2020 · Transportation Research Part C Emerging Technologies · 113 citations · first circulated 2018
with Qingyi Wang, Jinhua Zhao
Transportation Research Part C Emerging Technologies
Deep neural networks for choice analysis: Architecture design with alternative-specific utility functions
published2020 · Transportation Research Part C Emerging Technologies · 107 citations · first circulated 2019
with Baichuan Mo, Jinhua Zhao
Transportation Research Part C Emerging Technologies
Risk preference and adoption of autonomous vehicles
published2019 · Transportation Research Part A Policy and Practice · 138 citations
with Jinhua Zhao
Transportation Research Part A Policy and Practice
The distributional effects of lotteries and auctions—License plate regulations in Guangzhou
published2017 · Transportation Research Part A Policy and Practice · 25 citations
with Jinhua Zhao
Transportation Research Part A Policy and Practice

Assembled from arXiv and OpenAlex. Duplicate records for the same paper are merged, and the published version is shown where we could identify one. Corrections welcome.