80 citations · 90 across the 3 of their papers we have counts for
5 papers
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning
Yan Jiao, Xiaocheng Tang, Zhiwei Qin +4
We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-dem…
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem
John Holler, Risto Vuorio, Zhiwei Qin +6
Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ride-sharing platform…
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching
Ming Zhou, Jiarui Jin, Weinan Zhang +6
Improving the efficiency of dispatching orders to vehicles is a research hotspot in online ride-hailing systems. Most of the existing solutions for order-dispatching are centralize…
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms
Jiarui Jin, Ming Zhou, Weinan Zhang +9
How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-h…
Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning
Minne Li, Zhiwei, Qin +7
A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Tradition…