Publications

Journal Publications

Books and chapters

(1)        Ke, J., Yang, H., Wang, H., and Yin, Y., 2023. Supply and demand management of ride-sourcing markets, 1st Edition, Elsevier, DOI: https://doi.org/10.1016/C2022-0-01127-2.

Selected publications in peer-reviewed journals (Please see my google scholar for the full list of publications)

(1)        Chen, T., Shen, Z., Feng, S., Yang, L., and Ke, J., 2025. Dynamic adjustment of matching radii under the broadcasting mode: a novel multi-task learning strategy and temporal modeling approach. Transportation Research Part E, 193, 103822.

(2)        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.

(3)        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.  

(4)        Si, H., Liang, J., Ke, J., Cheng, L., De Vos, Jonas, 2024. What limits improper bike-sharing parking most: Penalties or incentives? Findings from an online behavioral experiment. Transportation Research Part F: Traffic Psychology and Behaviour, 107, 133–148.

(5)        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.

(6)        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.

(7)        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.

(8)        Liang, J., Zhao, Y., Wang, H., Xiao, Z., and Ke, J., 2024. Uncovering merchants’ willingness to wait in on-demand food delivery markets. Transport Policy, 158, 14–28.

(9)        Wong, R. C. P., Ke, J., Szeto, W. Y., and Mak, P. L., 2024. Multiple shared mobility services under competition: Empirical evidence for public acceptance and policy insights to sustainable transport. International Journal of Sustainable Transportation, 1–14.

(10)   Guo, S., Deng, B., Chen, C., Ke, J., Wang, J., Long, S., Xu, K., 2024. Seeking in ride-on-demand service: a reinforcement learning model with dynamic price prediction. IEEE Internet of Things Journal, in press.

(11)     Ke, J., Wang, H., Masoud, N., Schiffer, M., Corria, G., 2024. Editorial: Emerging on-demand passenger and logistics systems: Modelling, optimization, and data analytics. Transportation Research Part C: Emerging Technologies, 161, 104574.

(12)     Wang, Z., Ke, J., and Li, S., 2024. Planning and operation of ride-hailing networks with a mixture of level-4 autonomous vehicles and for-hire human drivers. Transportation Research Part C: Emerging Technologies, 160, 104541.

(13)     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.

(14)   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.

(15)     Chen, W., Gu, D., and Ke, J., 2023. Real-time ergonomic risk assessment in construction using a co-learning-powered 3d human pose estimation model. Computer-Aided Civil and Infrastructure Engineering, 1–17, https://doi.org/10.1111/mice.13139.

(16)     Liang, J., Ke, J.*, Wang, H., Ye, H., Tang, J., 2023. A Poisson-based Distribution Learning Framework for Short-term Prediction of Food Delivery Demand Ranges. IEEE Transactions on Intelligent Transportation System, 24(12), 14556–14569.

(17)     Vignon, D., Yin, Y., and Ke, J., 2023. Regulating the ride-hailing market in the age of uberization. Transportation Research Part E: Logistics and Transportation Review, 169, 102969.

(18)     Zhu, Z., Xu, M., Ke, J., Yang, H., and Chen, X. M., 2023. A Bayesian clustering ensemble Gaussian process model for network-wide traffic flow clustering and prediction. Transportation Research Part C: Emerging Technologies, 148, 104032.

(19)     Feng, S., Wei, S., Zhang, J., Li, Y., Ke, J., Chen, G., ... and Yang, H., 2023. A macro–micro spatio-temporal neural network for traffic prediction. Transportation Research Part C: Emerging Technologies, 156, 104331.

(20)     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.

(21)     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.

(22)     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.

(23)     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.

(24)     Qin, X., Ke, J., Wang, X., Tang, Y., & Yang, H., 2022. Demand management for smart transportation: A review. Multimodal Transportation, 1(4), 100038.

(25)     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.

(26)     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.

(27)     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.

(28)     Feng, S., Ke, J.*, Yang, H., 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, DOI: 10.1109/TITS.2021.3056415.

(29)     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 in Knowledge and Data Engineering, 34(5), 2280–2292, DOI: 10.1109/TKDE.2020.3006084.

(30)     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.

(31)     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.

(32)     Urata, J., Xu, Z., Ke, J., Yin, Y., Wu, G., Yang, H., and Ye, J., 2021. Learning ride-sourcing drivers’ customer-searching behavior: A dynamic discrete choice approach. Transportation Research Part C: Emerging Technologies, 130, 103293.

(33)     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.

(34)     Yang, L., Ao, Y., Ke, J., Lu, Y., and Liang, Y., 2021.To walk or not to walk? Examining non-linear effects of streetscape greenery on walking propensity of older adults. Journal of Transport Geography, 94, 103099.

(35)     Vignon, D., Yin, Y., and Ke, J., 2021. Regulating ride-sourcing services with product differentiation and congestion externality. Transportation Research Part C: Emerging Technologies, 127, 103088.

(36)     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. 

(37)     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.

(38)     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.

(39)     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.

(40)     Ke, J.*, Yang, H. and Zheng, Z., 2020. On ride-pooling and traffic congestion. Transportation Research Part B: Methodological, 142, 213–231.

(41)     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.

(42)     Chen, X., Zheng, H., Ke, J., and Yang, H., 2020. Dynamic optimization strategies for on-demand ride services platform: surge pricing, commission rate, and incentives. Transportation Research Part B: Methodological, 138, 23–45.

(43)     Zhu, Z., Qin, X., Ke, J., Zheng, Z. and Yang, H., 2020. Analyzing the impact of ridesplitting programs on multi-modal commute behavior based on a network model. Transportation Research Part A: Policy and Practice, 132, 713–727.

(44)     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.

(45)     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.

(46)     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.

(47)     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.

(48)     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.

(49)     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.  

(50)     Shao, C., Yang, H., Zhang, Y. and Ke, J., 2016. A simple reservation and allocation model of shared parking lots. Transportation Research Part C: Emerging Technologies, 71, 303–312.

* refers to the corresponding author, _ refers to the PhD students, postdocs, and research assistants supervised by Dr. Ke

Patents

1. Ke, J. et al., System and method for determining passenger-seeking ride-sourcing vehicle navigation. U.S. Patent, No. US 11,094,028 B2.

2. Ke, J. et al., A regional approach for predicting ride-hailing supply-demand gap (一种地理区域内网约车供需缺口预测方法). Chinese invention patent, No. CN109948822B.

3. Ke, J., Chen T., and Wang, J., An AI-based system for simulating a transportation network. U.S. Patent, Application No. 63/669,387, filed on 10 Jul 2024.