743 citations · 1.2k across the 11 of their papers we have counts for
11 papers
Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing
Wang Chen, Jintao Ke, Xiqun Chen
Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooke…
Scaling Laws of Dynamic High-Capacity Ride-Sharing
Wang Chen, Jintao Ke, Linchuan Yang
Dynamic ride-sharing services, including ride-pooling offered by ride-hailing platforms and demand-responsive buses, have become an essential part of urban mobility systems. These…
A multi-functional simulation platform for on-demand ride service operations
Siyuan Feng, Taijie Chen, Yuhao Zhang +3
On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been deve…
Joint predictions of multi-modal ride-hailing demands: a deep multi-task multigraph learning-based approach
Jintao Ke, Siyuan Feng, Zheng Zhu +2
Ride-hailing platforms generally provide various service options to customers, such as solo ride services, shared ride services, etc. It is generally expected that demands for diff…
Competitive ride-sourcing market with a third-party integrator
Yaqian Zhou, Hai Yang, Jintao Ke +2
Recently, some transportation service providers attempt to integrate the ride services offered by multiple independent ride-sourcing platforms, and passengers are able to request r…
Predicting origin-destination ride-sourcing demand with a spatio-temporal encoder-decoder residual multi-graph convolutional network
Jintao Ke, Xiaoran Qin, Hai Yang +3
With the rapid development of mobile-internet technologies, on-demand ride-sourcing services have become increasingly popular and largely reshaped the way people travel. Demand pre…
Optimizing Online Matching for Ride-Sourcing Services with Multi-Agent Deep Reinforcement Learning
Jintao Ke, Feng Xiao, Hai Yang +1
Ride-sourcing services are now reshaping the way people travel by effectively connecting drivers and passengers through mobile internets. Online matching between idle drivers and w…
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Huaxiu Yao, Fei Wu, Jintao Ke +6
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate re…
PCA-Based Missing Information Imputation for Real-Time Crash Likelihood Prediction Under Imbalanced Data
Jintao Ke, Shuaichao Zhang, Hai Yang +1
The real-time crash likelihood prediction has been an important research topic. Various classifiers, such as support vector machine (SVM) and tree-based boosting algorithms, have b…
Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach
Jintao Ke, Hongyu Zheng, Hai Yang +2
Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-dem…
Ridesourcing Car Detection by Transfer Learning
Leye Wang, Xu Geng, Jintao Ke +4
Ridesourcing platforms like Uber and Didi are getting more and more popular around the world. However, unauthorized ridesourcing activities taking advantages of the sharing economy…