Found 15 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.
Measuring Policy Leakage of Beijing's Car Ownership Restriction in Neighboring Cities. In: Transportation Research Board 99th Annual Meeting. Washington, D.C.; 2020.
Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data. Journal of Choice Modelling. 2020.
Predicting Travel Mode Choice with 86 Machine Learning Classifiers: An Empirical Benchmark Study. In: Transportation Research Board 99th Annual Meeting. Washington, D.C.; 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.