activity
20142020
most citedShort-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

743 citations · 994 across the 8 of their papers we have counts for

collaborators

9 papers

cs.LG2020★ 111 cited

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…

math.OC2020

Spatial Pricing in Ride-Sourcing Markets under a Congestion Charge

Sen Li, Hai Yang, Kameshwar Poolla +1

This paper studies the optimal spatial pricing for a ride-sourcing platform subject to a congestion charge. The platform determines the ride prices over the transportation network…

physics.soc-ph2020★ 81 cited

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: a pedestrian dynamics-based microscopic simulation approach

Yao Xiao, Mofeng Yang, Zheng Zhu +3

Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic contr…

physics.soc-ph2019★ 1 cited

Go left or right? Explore the side preference behavior with circle antipode experiments

Yao Xiao, Ziyou Gao, Rui Jiang +2

Side preference is a critical behavior in conflict handling, and here the behavior is investigated with pedestrian trajectories in circle antipode experiments that own both conflic…

eess.SP2019★ 25 cited

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…

cs.MA2019★ 30 cited

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…