Jintao KE
Jintao KE
Assistant Professor, the University of Hong Kong
Verified email at connect.ust.hk - Homepage
Title
Cited by
Cited by
Year
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
5562018
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
3912017
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
962016
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
592018
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
49*2020
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
402020
Learning to delay in ride-sourcing systems: a multi-agent deep reinforcement learning framework
KE Jintao, H Yang, J Ye
IEEE Transactions on Knowledge and Data Engineering, 2020
29*2020
A universal distribution law of network detour ratios
H Yang, J Ke, J Ye
Transportation Research Part C: Emerging Technologies 96, 22-37, 2018
282018
Modelling driversí 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
272019
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
232020
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
202021
On ride-pooling and traffic congestion
J Ke, H Yang, Z Zheng
Transportation Research Part B: Methodological 142, 213-231, 2020
162020
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
152020
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
122021
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
112019
Ridesourcing car detection by transfer learning
L Wang, X Geng, J Ke, C Peng, X Ma, D Zhang, Q Yang
arXiv preprint arXiv:1705.08409, 2017
72017
Regulating ridesourcing services with product differentiation and congestion externality
DA Vignon, Y Yin, J Ke
Transportation Research Part C: Emerging Technologies 127, 103088, 2021
62021
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
6*2021
Competitive ride-sourcing market with a third-party integrator
Y Zhou, H Yang, J Ke, H Wang, X Li
arXiv preprint arXiv:2008.09815, 2020
52020
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
32021
The system can't perform the operation now. Try again later.
Articles 1–20