AdaPool: A diurnal-adaptive fleet management framework using model-free deep reinforcement learning and change point detection
M Haliem, V Aggarwal… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
This paper introduces an adaptive model-free deep reinforcement approach that can
recognize and adapt to the diurnal patterns in the ride-sharing environment with car-pooling …
recognize and adapt to the diurnal patterns in the ride-sharing environment with car-pooling …
Multi-Agent Mix Hierarchical Deep Reinforcement Learning for Large-Scale Fleet Management
X Huang, J Ling, X Yang, X Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In recent years, ride-sharing has gained popularity as a daily means of transportation. The
primary challenge for large-scale online ride-sharing platforms is to design an efficient fleet …
primary challenge for large-scale online ride-sharing platforms is to design an efficient fleet …
Using reinforcement learning to minimize taxi idle times
K O'Keeffe, S Anklesaria, P Santi… - Journal of Intelligent …, 2022 - Taylor & Francis
Taxis spend a large amount of time idle, searching for passengers. The routes vacant taxis
should follow in order to minimize their idle times are hard to calculate; they depend on …
should follow in order to minimize their idle times are hard to calculate; they depend on …
InBEDE: Integrating contextual bandit with TD learning for joint pricing and dispatch of ride-hailing platforms
For both the traditional street-hailing taxi industry and the recently emerged on-line ride-
hailing, it has been a major challenge to improve the ride-hailing marketplace efficiency due …
hailing, it has been a major challenge to improve the ride-hailing marketplace efficiency due …
Deep reinforcement learning-based trajectory pricing on ride-hailing platforms
Dynamic pricing plays an important role in solving the problems such as traffic load
reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies …
reduction, congestion control, and revenue improvement. Efficient dynamic pricing strategies …
Reinforcement learning in the wild: Scalable RL dispatching algorithm deployed in ridehailing marketplace
In this study, a scalable and real-time dispatching algorithm based on reinforcement
learning is proposed and for the first time, is deployed in large scale. Current dispatching …
learning is proposed and for the first time, is deployed in large scale. Current dispatching …
Efficient ridesharing order dispatching with mean field multi-agent reinforcement learning
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. Traditional rule …
and efficiently, dispatch user's ride requests to the right driver in real time. Traditional rule …
Can sophisticated dispatching strategy acquired by reinforcement learning?-a case study in dynamic courier dispatching system
Y Chen, Y Qian, Y Yao, Z Wu, R Li, Y Zhou… - arXiv preprint arXiv …, 2019 - arxiv.org
In this paper, we study a courier dispatching problem (CDP) raised from an online pickup-
service platform of Alibaba. The CDP aims to assign a set of couriers to serve pickup …
service platform of Alibaba. The CDP aims to assign a set of couriers to serve pickup …
Deep reinforcement learning for multi-driver vehicle dispatching and repositioning problem
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 …
spatially and temporally varying supply and demand are central to a ride-sharing platform …
Learning to delay in ride-sourcing systems: A multi-agent deep reinforcement learning framework
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 …
drivers and passengers through mobile internets. Online matching between idle drivers and …