An Enhanced Approximate Dynamic Programming Approach to On-demand Ride Pooling
Ride-pooling services have been growing in popularity, increasing the need for efficient and
effective operations. The main goal of ride-pooling services is to maximize the number of …
effective operations. The main goal of ride-pooling services is to maximize the number of …
Deeppool: Distributed model-free algorithm for ride-sharing using deep reinforcement learning
AO Al-Abbasi, A Ghosh… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The success of modern ride-sharing platforms crucially depends on the profit of the ride-
sharing fleet operating companies, and how efficiently the resources are managed. Further …
sharing fleet operating companies, and how efficiently the resources are managed. Further …
AdaPool: An adaptive model-free ride-sharing approach for dispatching using deep reinforcement learning
M Haliem, V Aggarwal, B Bhargava - Proceedings of the 7th ACM …, 2020 - dl.acm.org
Deep Reinforcement Learning (RL) suffer from catastrophic forgetting due to being agnostic
to the timescale of changes in the distribution of experiences. Although, RL algorithms are …
to the timescale of changes in the distribution of experiences. Although, RL algorithms are …
Reinforcement learning from optimization proxy for ride-hailing vehicle relocation
Idle vehicle relocation is crucial for addressing demand-supply imbalance that frequently
arises in the ride-hailing system. Current mainstream methodologies-optimization and …
arises in the ride-hailing system. Current mainstream methodologies-optimization and …
MOVI: A model-free approach to dynamic fleet management
T Oda, C Joe-Wong - IEEE INFOCOM 2018-IEEE Conference …, 2018 - ieeexplore.ieee.org
Modern vehicle fleets, eg, for ridesharing platforms and taxi companies, can reduce
passengers' waiting times by proactively dispatching vehicles to locations where pickup …
passengers' waiting times by proactively dispatching vehicles to locations where pickup …
A Survey of Machine Learning-Based Ride-Hailing Planning
D Wen, Y Li, F Lau - arXiv preprint arXiv:2303.14646, 2023 - arxiv.org
Ride-hailing is a sustainable transportation paradigm where riders access door-to-door
traveling services through a mobile phone application, which has attracted a colossal …
traveling services through a mobile phone application, which has attracted a colossal …
An integrated reinforcement learning and centralized programming approach for online taxi dispatching
Balancing the supply and demand for ride-sourcing companies is a challenging issue,
especially with real-time requests and stochastic traffic conditions of large-scale congested …
especially with real-time requests and stochastic traffic conditions of large-scale congested …
DROP: Deep relocating option policy for optimal ride-hailing vehicle repositioning
In a ride-hailing system, an optimal relocation of vacant vehicles can significantly reduce
fleet idling time and balance the supply–demand distribution, enhancing system efficiency …
fleet idling time and balance the supply–demand distribution, enhancing system efficiency …
A Survey of Machine Learning-Based Ride-Hailing Planning
D Wen, Y Li, FCM Lau - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
Ride-hailing is a sustainable transportation paradigm where riders access door-to-door
traveling services through a mobile phone application, which has attracted a colossal …
traveling services through a mobile phone application, which has attracted a colossal …
Learning-Augmented Vehicle Dispatching with Slack Times for High-Capacity Ride-Pooling
Y Kim, V Jayawardana… - Available at SSRN …, 2024 - papers.ssrn.com
Abstract Pooled Mobility-on-demand (MoD) services have emerged with numerous benefits,
including reducing carbon emissions and providing affordable transportation options …
including reducing carbon emissions and providing affordable transportation options …