Found 22 results
Filters: Author is Shenhao Wang [Clear All Filters]
Alleviating Data Sparsity Problems in Estimated Time of Arrival via Auxiliary Metric Learning. IEEE Transactions on Intelligent Transportation Systems. 2022.
Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective. Transportation Research Part B. 2021.
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models. Transportation Research Part C. 2021.
Measuring policy leakage of Beijing’s car ownership restriction. Transportation Research Part A: Policy and Practice. 2021.
The Relationship between Transportation Network Companies and Public Transit in Chicago: a comparison before and after COVID-19 shutdowns. Journal of Transport Geography. 2021.
Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks. Transportation Research Part B. 2021.
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.