Found 13 results
Filters: Author is Shenhao Wang [Clear All Filters]
Deep Neural Networks for Choice Analysis: Architecture Design with Alternative-Specific Utility Functions. Transportation Research Part C. 2020.
Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation. Transportation Research Part C: Emerging Technologies. 2020.
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data. Journal of Choice Modelling. 2020.
What prompts the adoption of car restriction policies among Chinese cities. International Journal of Sustainable Transportation. 2020.
Deep Neural Networks for Choice Analysis. Vol PhD.; 2019.
Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective. Working Paper. 2019.
Risk Preference and Adoption of Autonomous Vehicles. Transportation Research Part A. 2019.
Transportation Policy Profiles of Chinese City Clusters: A Mixed Method Approach. Transportation Research Interdisciplinary Perspectives. 2019.
How Risk Preferences Influence the Usage of Autonomous Vehicles. In: Transportation Research Board 97th Annual Meeting. Washington, D.C.; 2018.
Trajectories of Urban Development and Motorization: Clustering 287 Chinese Cities. In: Transportation Research Board 97th Annual Meeting. Washington, D.C.; 2018.
Automobile Regulations in China Examined from a Behavioral Perspective. Vol Master in City Planning, Master of Science in Transportation. Cambridge, MA: Massachusetts Institute of Technology; 2017.
Distributional Effects of Lotteries and Auctions —License Plate Regulations in Guangzhou. Transportation Research Part A: Policy and Practice. 2017;106:473-483.