Qingyi Wang, Shenhao Wang, Yunhan Zheng, Lin H, Xiaohu Zhang, Jinhua Zhao, et al. Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for travel behavior analysis? Transportation Research Part B. 2024;179.

Qingyi Wang. Innovations in Urban Computing: Uncertainty Quantification, Data Fusion, and Generative Urban Design. Vol PhD.; 2024.

Qingyi Wang, Shenhao Wang, Zhuang D, Haris Koutsopoulos, Jinhua Zhao. Uncertainty Quantification of Spatiotemporal Travel Demand With Probabilistic Graph Neural Networks. IEEE Transactions on Intelligent Transportation Systems. 2024.

Yunhan Zheng, Qingyi Wang, Zhuang D, Shenhao Wang, Jinhua Zhao. Fairness-enhancing deep learning for ride-hailing demand prediction. IEEE Open Journal of Intelligent Transportation Systems . 2023;4.

Xiaotong Guo, Qingyi Wang, Jinhua Zhao. Data-driven Vehicle Rebalancing with Predictive Prescriptions in the Ride-hailing System. IEEE Open Journal of Intelligent Transportation Systems. 2022.

Shenhao Wang, Qingyi Wang, Nate Bailey, Jinhua Zhao. Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective. Transportation Research Part B. 2021.

Shenhao Wang, Qingyi Wang, Jinhua Zhao. Deep Neural Networks for Choice Analysis: Extracting Complete Economic Information for Interpretation. Transportation Research Part C: Emerging Technologies. 2020.

Shenhao Wang, Qingyi Wang, Jinhua Zhao. Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data. Journal of Choice Modelling. 2020.

Qingyi Wang, Haris Koutsopoulos, Nigel Wilson. Unexpected Bus Operator Absence and Extraboard Scheduling – MBTA Case Study. In: Transportation Research Board 99th Annual Meeting. Washington, D.C.; 2020.

Shenhao Wang, Qingyi Wang, Jinhua Zhao. Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective. Working Paper. 2019.