Jintao Ke
Orcid: 0000-0001-9778-3387
According to our database1,
Jintao Ke
authored at least 18 papers
between 2017 and 2024.
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Bibliography
2024
Seeking in Ride-on-Demand Service: A Reinforcement Learning Model With Dynamic Price Prediction.
IEEE Internet Things J., September, 2024
Real-time ergonomic risk assessment in construction using a co-learning-powered 3D human pose estimation model.
Comput. Aided Civ. Infrastructure Eng., May, 2024
2023
A Poisson-Based Distribution Learning Framework for Short-Term Prediction of Food Delivery Demand Ranges.
IEEE Trans. Intell. Transp. Syst., December, 2023
Dynamic Adjustment of Matching Radii under the Broadcasting Mode: A Novel Multitask Learning Strategy and Temporal Modeling Approach.
CoRR, 2023
Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing.
CoRR, 2023
CoRR, 2023
2022
Learning to Delay in Ride-Sourcing Systems: A Multi-Agent Deep Reinforcement Learning Framework.
IEEE Trans. Knowl. Data Eng., 2022
A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand.
IEEE Trans. Intell. Transp. Syst., 2022
2020
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach.
CoRR, 2020
2019
Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services.
IEEE Trans. Intell. Transp. Syst., 2019
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network.
CoRR, 2019
Optimizing Online Matching for Ride-Sourcing Services with Multi-Agent Deep Reinforcement Learning.
CoRR, 2019
2018
PCA-Based Missing Information Imputation for Real-Time Crash Likelihood Prediction Under Imbalanced Data.
CoRR, 2018
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018
2017
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach.
CoRR, 2017