| Deep neural networks for choice analysis: Extracting complete economic information for interpretation S Wang, Q Wang, J Zhao Transportation Research Part C: Emerging Technologies 118, 102701, 2020 | 105 | 2020 |
| Deep neural networks for choice analysis: A statistical learning theory perspective S Wang, Q Wang, N Bailey, J Zhao Transportation Research Part B: Methodological 148, 60-81, 2021 | 40 | 2021 |
| Multitask learning deep neural networks to combine revealed and stated preference data S Wang, Q Wang, J Zhao Journal of choice modelling 37, 100236, 2020 | 39 | 2020 |
| Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks Q Wang, S Wang, D Zhuang, H Koutsopoulos, J Zhao IEEE Transactions on Intelligent Transportation Systems, 2024 | 27 | 2024 |
| Impacts of subjective evaluations and inertia from existing travel modes on adoption of autonomous mobility-on-demand B Mo, QY Wang, J Moody, Y Shen, J Zhao Transportation Research Part C: Emerging Technologies 130, 103281, 2021 | 25 | 2021 |
| Data-driven vehicle rebalancing with predictive prescriptions in the ride-hailing system X Guo, Q Wang, J Zhao IEEE Open Journal of Intelligent Transportation Systems 3, 251-266, 2022 | 24 | 2022 |
| Predicting drivers’ route trajectories in last-mile delivery using a pair-wise attention-based pointer neural network B Mo, Q Wang, X Guo, M Winkenbach, J Zhao Transportation Research Part E: Logistics and Transportation Review 175, 103168, 2023 | 18 | 2023 |
| Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis? Q Wang, S Wang, Y Zheng, H Lin, X Zhang, J Zhao, J Walker Transportation Research Part B: Methodological 179, 102869, 2024 | 12 | 2024 |
| Fairness-enhancing deep learning for ride-hailing demand prediction Y Zheng, Q Wang, D Zhuang, S Wang, J Zhao IEEE Open Journal of Intelligent Transportation Systems 4, 551-569, 2023 | 12 | 2023 |
| Amazon last-mile delivery trajectory prediction using hierarchical TSP with customized cost matrix X Guo, B Mo, Q Wang arXiv preprint arXiv:2302.02102, 2023 | 5 | 2023 |
| Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition D Zhang, Q Wang, S Song, S Chen, M Li, L Shen, S Zheng, B Cai, ... Iscience 26 (9), 2023 | 3 | 2023 |
| Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network D Zhuang, Q Wang, Y Zheng, X Guo, S Wang, HN Koutsopoulos, J Zhao arXiv preprint arXiv:2405.14079, 2024 | 2 | 2024 |
| Estimating air quality co-benefits of energy transition using machine learning D Zhang, Q Wang, S Song, S Chen, M Li, L Shen, S Zheng, B Cai, ... arXiv preprint arXiv:2105.14318, 2021 | | 2021 |
| Advancing Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network D Zhuang, Q Wang, Y Zheng, X Guo, S Wang, HN Koutsopoulos, J Zhao Available at SSRN 4764727, 0 | | |
Jinhua ZhaoProfessor of Cities and Transportation, Massachusetts Institute of TechnologyEmail yang diverifikasi di mit.edu