Research

By June 2026, Dr. Ke has co-authored over 60 peer-reviewed journal articles. His papers have received more than 6,800 citations with an H-index of 35 according to Google Scholar. He was ranked among the top 2% of scientists by Stanford University and Elsevier from 2023 to 2025, currently ranking 254th out of 30,588 authors in the subfield of Logistics and Transportation.

Notes: * indicates corresponding author. Underlined authors are RAPs, Postdocs, Ph.D., M.Phil., or RAs supervised by Dr. Ke.

Book

• Ke, J., Yang, H., Wang, H., and Yin, Y., 2023. Supply and Demand Management of Ride-Sourcing Markets, 1st Edition, Elsevier, DOI: 10.1016/C2022-0-01127-2.

Selected Journal Publications

• Zhang, K., Ke, J.*, and Wang, X., 2026. A three-sided network equilibrium model for on-demand food delivery services. Transportation Research Part B: Methodological, 209, 103461.
• Chen, T., Shen, Z., Zhou, B., Liu, Y., Wang, S., and Ke, J.*, 2026. Addressing online incremental transport mode choice prediction problem with an LLM-augmented class-incremental learning approach. Transportation Research Part C: Emerging Technologies, 188, 105709.
• Liang, J., Ke, J.*, Feng, S., and Yang, H., 2025. Machine learning for on-demand ride services: A survey. Artificial Intelligence for Transportation, 2, 100008.
• Liang, J. and Ke, J.*, 2025. A Markov decision process framework for order dispatching in on-demand delivery services. Multimodal Transportation, 100240.
• Chen, W., Ke, J.*, Yang, L., and Chen, X., 2025. Scaling laws of dynamic high-capacity ride-sharing. Transportation Research Part C: Emerging Technologies, 174, 105064.
• Zhang, K., Ke, J.*, Wang, H., and Yin, Y., 2025. Tactical operations of service region dimensioning, bundling, and matching for on-demand food delivery services. Transportation Research Part C: Emerging Technologies, 174, 105069.
• Qin, X., Ke, J.*, and Yang, H., 2025. Government regulations for ride-sourcing services as substitute or complement to public transit. Transport Policy, 172, 103776.
• Wang, C. and Ke, J.*, 2024. Modelling a ride-sourcing market with a third-party platform integrator under batch matching mechanisms. Transportation Research Part E: Logistics and Transportation Review, 192, 103803.
• Chen, W., Ke, J.*, and Chen, X., 2024. Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing. Transportmetrica B: Transport Dynamics, 12(1), 2423235.
• Feng, S., Chen, T., Zhang, Y., Ke, J.*, Zheng, Z., and Yang, H., 2024. A multi-functional simulation platform for on-demand ride service operations. Communications in Transportation Research, 4, 100141.
• Zhou, Y., Ke, J.*, Yang, H., and Guo, P., 2024. Platform integration for ride-sourcing markets with heterogeneous customers. Transportation Research Part B: Methodological, 188, 103041.
• Li, X., Ke, J.*, Yang, H., and Wang, H., 2024. An aggregate matching and pick-up model for mobility-on-demand services. Transportation Research Part B: Methodological, 190, 103070.
• Ke, J., Wang, H., Masoud, N., Schiffer, M., and Correia, G., 2024. Editorial: Emerging on-demand passenger and logistics systems: Modelling, optimization, and data analytics. Transportation Research Part C: Emerging Technologies, 161, 104574.
• Ke, J., Wang, C., Li, X., Tian, Q., and Huang, H., 2024. Equilibrium analysis for on-demand food delivery markets. Transportation Research Part E: Logistics and Transportation Review, 184, 103467.
• Chen, Z., Miu, Y., Ke, J.*, and He, Q., 2024. Operations and regulations for a ride-sourcing market with a mixed fleet of human drivers and autonomous vehicles. Transportation Research Part C: Emerging Technologies, 160, 104519.
• Liang, J., Ke, J.*, Wang, H., Ye, H., and Tang, J., 2023. A Poisson-based distribution learning framework for short-term prediction of food delivery demand ranges. IEEE Transactions on Intelligent Transportation Systems, 24(12), 14556-14569.
• Li, X., Yang, H., and Ke, J.*, 2023. Booking cum rationing strategy for equitable travel demand management in road networks. Transportation Research Part B: Methodological, 167, 261-274.
• Feng, S., Ke, J.*, Xiao, F., and Yang, H., 2022. Approximating a ride-sourcing system with block matching. Transportation Research Part C: Emerging Technologies, 145, 103920.
• Ke, J., Yang, H., Chen, X., and Li, S., 2022. Coordinating supply and demand in ride-sourcing markets with pooling service and traffic congestion externality. Transportation Research Part E: Logistics and Transportation Review, 166, 102887.
• Zhou, Y., Yang, H., and Ke, J.*, 2022. Price of competition and fragmentation in ride-sourcing markets. Transportation Research Part C: Emerging Technologies, 143, 103851.
• Wei, S., Feng, S., Ke, J.*, and Yang, H., 2022. Calibration and validation of matching functions for ride-sourcing markets. Communications in Transportation Research, 2, 100058.
• Feng, S., Duan, P., Ke, J.*, and Yang, H., 2022. Coordinating ride-sourcing and public transport services with a reinforcement learning approach. Transportation Research Part C: Emerging Technologies, 138, 103611.
• Zhou, Y., Yang, H., Ke, J.*, Wang, H., and Li, X., 2022. Competition and third-party platform-integration in ride-sourcing markets. Transportation Research Part B: Methodological, 159, 76-103.
• Feng, S., Ke, J.*, Yang, H., and Ye, J., 2022. A multi-task matrix factorized graph neural network for co-prediction of zone-based and OD-based ride-hailing demand. IEEE Transactions on Intelligent Transportation Systems, 23(6), 5704-5716.
• Ke, J., Xiao, F., Yang, H., and Ye, J., 2022. Learning to delay in ride-sourcing systems: a multi-agent deep reinforcement learning framework. IEEE Transactions on Knowledge and Data Engineering, 34(5), 2280-2292.
• Zhao, Y., and Ke, J.*, 2021. The impact of shared mobility services on housing values near subway stations. Transportation Research Part D: Transport and Environment, 101, 103097.
• Ke, J., Li, X., Yang, H., and Yin, Y., 2021. Pareto-efficient solutions and regulations of congested ride-sourcing markets with heterogeneous demand and supply. Transportation Research Part E: Logistics and Transportation Review, 154, 102483.
• Zhu, Z., Ke, J.*, and Wang, H., 2021. A mean-field Markov decision process model for spatial-temporal subsidies in ride-sourcing markets. Transportation Research Part B: Methodological, 150, 540-565.
• Ke, J., Feng, S., Zhu, Z., Yang, H., and Ye, J., 2021. Joint predictions of ride-hailing demands for multiple service modes with a deep multi-task multi-graph learning framework. Transportation Research Part C: Emerging Technologies, 127, 103063.
• Ke, J., Zhu, Z., Yang, H., and He, Q., 2021. Equilibrium analyses and operational designs of a coupled market with substitutive and complementary ride-sourcing services to public transits. Transportation Research Part E: Logistics and Transportation Review, 148, 102236.
• Ke, J., Zheng, Z., Yang, H., and Ye, J., 2021. Data-driven analysis of matching probability, routing distance and detour distance in on-demand ride-pooling services. Transportation Research Part C: Emerging Technologies, 124, 102922.
• Ke, J., Qin, X., Yang, H., Zheng, Z., Zhu, Z., and Ye, J., 2021. Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network. Transportation Research Part C: Emerging Technologies, 122, 102858.
• Ke, J.*, Yang, H., and Zheng, Z., 2020. On ride-pooling and traffic congestion. Transportation Research Part B: Methodological, 142, 213-231.
• Ke, J., Yang, H., Li, X., Wang, H., and Ye, J., 2020. Pricing and equilibrium in on-demand ride-pooling markets. Transportation Research Part B: Methodological, 139, 411-431.
• Yang, H., Qin, X., Ke, J.*, and Ye, J., 2019. Optimizing matching time interval and matching radius in on-demand ride-sourcing markets. Transportation Research Part B: Methodological, 131, 84-105.
• Ke, J., Cen, X., Yang, H., Chen, X., and Ye, J., 2019. Modelling drivers’ working and recharging schedules in a ride-sourcing market with electric vehicles and gasoline vehicles. Transportation Research Part E: Logistics and Transportation Review, 125, 160-180.
• Ke, J., Yang, H., Zheng, H., Chen, X., Jia, Y., Gong, P., and Ye, J., 2019. Hexagon-based convolutional neural network for supply-demand forecasting of ride-sourcing services. IEEE Transactions on Intelligent Transportation Systems, 20(11), 4160-4173.
• Ke, J., Zhang, S., Yang, H., and Chen, X., 2018. PCA-based missing information imputation for real-time crash likelihood prediction under imbalanced data. Transportmetrica A: Transport Science, 15(2), 872-895.
• Yang, H., Ke, J.*, and Ye, J., 2018. A universal distribution law of network detour ratios. Transportation Research Part C: Emerging Technologies, 96, 22-37.
• Ke, J., Zheng, H., Yang, H., and Chen, X., 2017. Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. Transportation Research Part C: Emerging Technologies, 85, 591-608.

Selected Conference Papers

• Chen, T., Su, R., Feng, S., Zhang, L., Zhang, H., Wang, H., Ma, Z., and Ke, J.*, 2026. D3-Subsidy: Online and sequential driver subsidy decision-making for large-scale ride-hailing market. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD 2026), Jeju, Korea, August 9-13, 2026.
• Zhu, Z., Ke, J.*, and Wang, H., 2021. A mean-field Markov decision process for ride-sourcing modeling: optimization of spatial subsidy and idle vehicle relocation. In Proceedings of the 24th International Symposium on Transportation and Traffic Theory (ISTTT24).
• Yao, H., Wu, F., Ke, J., Tang, X., Jia, Y., Lu, S., Gong, P., and Ye, J., 2018. Deep multi-view spatial temporal network for taxi demand prediction. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, United States.

Patents

• System and method for determining passenger-seeking ride-sourcing vehicle navigation, U.S. Patent No. US 11,094,028 B2.
• A regional approach for predicting ride-hailing supply-demand gap, Chinese Invention Patent No. CN109948822B.

Full Publication List

For the full and most up-to-date publication list, please visit Google Scholar.

Grants

Since my entry into HKU in 2021, I have acquired over $14 million HKD from 10 external research grants as a principal investigator (PI) or Project Coordinator (PC), and over $20 million HKD as a Co-PI or Co-I.

Selected external grants as PI include:

• Smart Traffic Fund: “SmartSim: AI-assisted Simulation Software for Multimodal Transportation Operations”, 2024-2026.
• MTR Research Funding: “Multimodal traffic simulation, route recommendation and subsidy: Enhancing first and last mile connectivity for MTR”, 2026-2028.
• Environment and Conservation Fund Research and Development Projects, 245/2025: “Designing an Integrated Ground-air Monitoring System for Urban Greenhouse Gases and Air Pollutant Emissions Using Coordinated UAV and Taxi Scheduling”, 2026-2028.
• Highways Department: “Provision of Services for Independent Review on Application of Artificial Intelligence (AI) on Railway Development Study (RDS) Model to the Highways Department as detailed in the Service Specification”, 2026.
• RGC General Research Fund: “Matching strategy designs for e-hailing taxi markets under the broadcasting mechanism”, 2026-2028.
• Public Policy Research Funding Scheme: “A Planning Scheme of Commercial Electric Vehicle Chargers for Promoting Green Transport in Hong Kong”, 2024-2025.
• RGC Early Career Scheme: “Modelling, simulation, and operational strategy designs for on-demand food delivery services”, 2024-2026.
• Environment Conservation Fund: “Estimating carbon emissions, assessing decarbonization strategies and managing green transportation in Hong Kong with a multifunctional simulation platform”, 2024-2026.
• Smart Traffic Fund: “Development of a Simulation Platform and Artificial Intelligent Algorithms for Optimising Operation and Management of Taxi E-hailing Services”, 2023-2024.
• NSFC Young Scientists Fund: “Management and optimization for ride-sourcing markets under a mix of autonomous vehicles and human drivers”, 2023-2025.
• RGC General Research Fund: “Pareto efficient regulatory schemes for ride-sourcing markets”, 2022-2024.