| Risk preference and adoption of autonomous vehicles S Wang, J Zhao Transportation research part A: policy and practice 126, 215-229, 2019 | 18 | 2019 |
| Deep neural networks for choice analysis: Extracting complete economic information for interpretation S Wang, Q Wang, J Zhao Transportation Research Part C: Emerging Technologies 118, 102701, 2020 | 9* | 2020 |
| The distributional effects of lotteries and auctions—License plate regulations in Guangzhou S Wang, J Zhao Transportation Research Part A: Policy and Practice 106, 473-483, 2017 | 6 | 2017 |
| Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective S Wang, J Zhao arXiv preprint arXiv:1810.10465, 2018 | 5* | 2018 |
| Deep neural networks for choice analysis: Architecture design with alternative-specific utility functions S Wang, B Mo, J Zhao Transportation Research Part C: Emerging Technologies 112, 234-251, 2020 | 4 | 2020 |
| Transportation policy profiles of Chinese city clusters: A mixed methods approach J Moody, S Wang, J Chun, X Ni, J Zhao Transportation Research Interdisciplinary Perspectives 2, 100053, 2019 | 4 | 2019 |
| Multitask learning deep neural networks to combine revealed and stated preference data S Wang, Q Wang, J Zhao Journal of Choice Modelling 37, 100236, 2020 | 3 | 2020 |
| What prompts the adoption of car restriction policies among Chinese cities S Wang, J Moody, J Zhao International Journal of Sustainable Transportation, 1-12, 2020 | 1 | 2020 |
| Divergent Trajectories of Urban Development in 287 Chinese Cities S Wang, J Zhao OSF Preprints, 2018 | 1 | 2018 |
| Superficial Fairness of Transport Policies: Beijing’s Car License Lottery J Zhao, S Wang transportation 51, 2011, 2012 | 1 | 2012 |
| Automobile regulations in China examined from a behavioral perspective S Wang Massachusetts Institute of Technology, 2017 | | 2017 |
Jinhua ZhaoDepartment of Urban Studies and Planning, MITVerified email at mit.edu
