Shenhao Wang
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for travel behavior analysis? Transportation Research Part B. 2024;179.
Impacts of remote work on vehicle miles traveled and transit ridership in the USA. Nature Cities. 2024.
Cooperative Bus Holding and Stop-skipping: A Deep Reinforcement Learning Framework. Transportation Research Part C: Emerging Technologies. 2023;155.
Fairness-enhancing deep learning for ride-hailing demand prediction. IEEE Open Journal of Intelligent Transportation Systems . 2023;4.
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks. Temporal Graph Learning Workshop @ NeurIPS 2023. 2023.
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks. 2023 IEEE 26th International Conference on Intelligent Transportation Systems. 2023.
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.