| Deep multi-view spatial-temporal network for taxi demand prediction H Yao, F Wu, J Ke, X Tang, Y Jia, S Lu, P Gong, J Ye, Z Li Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 1296 | 2018 |
| Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach J Ke, H Zheng, H Yang, XM Chen Transportation research part C: Emerging technologies 85, 591-608, 2017 | 781 | 2017 |
| To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults L Yang, Y Ao, J Ke, Y Lu, Y Liang Journal of transport geography 94, 103099, 2021 | 284 | 2021 |
| A simple reservation and allocation model of shared parking lots C Shao, H Yang, Y Zhang, J Ke Transportation Research Part C: Emerging Technologies 71, 303-312, 2016 | 210 | 2016 |
| Pricing and equilibrium in on-demand ride-pooling markets J Ke, H Yang, X Li, H Wang, J Ye Transportation Research Part B: Methodological 139, 411-431, 2020 | 188 | 2020 |
| Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network J Ke, X Qin, H Yang, Z Zheng, Z Zhu, J Ye Transportation Research Part C: Emerging Technologies 122, 102858, 2021 | 169 | 2021 |
| Optimizing matching time interval and matching radius in on-demand ride-sourcing markets H Yang, X Qin, J Ke, J Ye Transportation Research Part B: Methodological 131, 84-105, 2020 | 165 | 2020 |
| Hexagon-based convolutional neural network for supply-demand forecasting of ride-sourcing services J Ke, H Yang, H Zheng, X Chen, Y Jia, P Gong, J Ye IEEE Transactions on Intelligent Transportation Systems 20 (11), 4160-4173, 2018 | 162 | 2018 |
| Learning to delay in ride-sourcing systems: A multi-agent deep reinforcement learning framework J Ke, F Xiao, H Yang, J Ye IEEE Transactions on Knowledge and Data Engineering 34 (5), 2280-2292, 2020 | 139* | 2020 |
| On ride-pooling and traffic congestion J Ke, H Yang, Z Zheng Transportation Research Part B: Methodological 142, 213-231, 2020 | 105 | 2020 |
| Dynamic optimization strategies for on-demand ride services platform: Surge pricing, commission rate, and incentives XM Chen, H Zheng, J Ke, H Yang Transportation Research Part B: Methodological 138, 23-45, 2020 | 100 | 2020 |
| Joint predictions of multi-modal ride-hailing demands: A deep multi-task multi-graph learning-based approach J Ke, S Feng, Z Zhu, H Yang, J Ye Transportation Research Part C: Emerging Technologies 127, 103063, 2021 | 86 | 2021 |
| A universal distribution law of network detour ratios H Yang, J Ke, J Ye Transportation Research Part C: Emerging Technologies 96, 22-37, 2018 | 82 | 2018 |
| Modelling drivers’ working and recharging schedules in a ride-sourcing market with electric vehicles and gasoline vehicles J Ke, X Cen, H Yang, X Chen, J Ye Transportation Research Part E: Logistics and Transportation Review 125, 160-180, 2019 | 79 | 2019 |
| Competition and third-party platform-integration in ride-sourcing markets Y Zhou, H Yang, J Ke, H Wang, X Li Transportation Research Part B: Methodological 159, 76-103, 2022 | 76* | 2022 |
| Analysis of multi-modal commute behavior with feeding and competing ridesplitting services Z Zhu, X Qin, J Ke, Z Zheng, H Yang Transportation Research Part A: Policy and Practice 132, 713-727, 2020 | 74 | 2020 |
| A mean-field Markov decision process model for spatial-temporal subsidies in ride-sourcing markets Z Zhu, J Ke, H Wang Transportation Research Part B: Methodological 150, 540-565, 2021 | 66 | 2021 |
| Data-driven analysis on matching probability, routing distance and detour distance in ride-pooling services J Ke, Z Zheng, H Yang, J Ye Transportation Research Part C: Emerging Technologies 124, 102922, 2021 | 61 | 2021 |
| A multi-task matrix factorized graph neural network for co-prediction of zone-based and OD-based ride-hailing demand S Feng, J Ke, H Yang, J Ye IEEE Transactions on Intelligent Transportation Systems 23 (6), 5704-5716, 2021 | 50 | 2021 |
| PCA-based missing information imputation for real-time crash likelihood prediction under imbalanced data J Ke, S Zhang, H Yang, X Chen Transportmetrica A: transport science 15 (2), 872-895, 2019 | 50 | 2019 |
Hai YangThe Hong Kong University of Science and TechnologyVerified email at ust.hk
