Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare
Large events such as conferences, concerts and sports games, often cause surges in
demand for ride services that are not captured in average demand patterns, posing unique …
demand for ride services that are not captured in average demand patterns, posing unique …
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 …
Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services
The popularity of ride-hailing platforms has significantly improved travel efficiency by
providing convenient and personalized transportation services. Designing an effective ride …
providing convenient and personalized transportation services. Designing an effective ride …
On Sustainable Ride Pooling Through Conditional Expected Value Decomposition
Abstract Centralized Multi-Agent Reinforcement Learning (MARL) presents itself as an ideal
framework for aggregation companies (eg, Uber, Lyft, Deliveroo) that have to take a …
framework for aggregation companies (eg, Uber, Lyft, Deliveroo) that have to take a …
Reinforcement learning for ridesharing: An extended survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to decision optimization problems in a typical ridesharing system …
learning approaches to decision optimization problems in a typical ridesharing system …
Efficient ridesharing dispatch using multi-agent reinforcement learning
O De Lima, H Shah, TS Chu, B Fogelson - arXiv preprint arXiv:2006.10897, 2020 - arxiv.org
With the advent of ride-sharing services, there is a huge increase in the number of people
who rely on them for various needs. Most of the earlier approaches tackling this issue …
who rely on them for various needs. Most of the earlier approaches tackling this issue …
[PDF][PDF] Distributed Service Area Control for Ride Sharing by using Multi-Agent Deep Reinforcement Learning.
N Yoshida, I Noda, T Sugawara - ICAART (1), 2021 - scitepress.org
We propose a decentralized system to determine where ride-sharing vehicle agents should
wait for passengers using multi-agent deep reinforcement learning. Although numerous …
wait for passengers using multi-agent deep reinforcement learning. Although numerous …
A multi-functional simulation platform for on-demand ride service operations
On-demand ride services or ride-sourcing services have been experiencing fast
development in the past decade. Various mathematical models and optimization algorithms …
development in the past decade. Various mathematical models and optimization algorithms …
A distributed model-free ride-sharing approach for joint matching, pricing, and dispatching using deep reinforcement learning
M Haliem, G Mani, V Aggarwal… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Significant development of ride-sharing services presents a plethora of opportunities to
transform urban mobility by providing personalized and convenient transportation while …
transform urban mobility by providing personalized and convenient transportation while …
Optimizing Long-Term Efficiency and Fairness in Ride-Hailing under Budget Constraint via Joint Order Dispatching and Driver Repositioning
Ride-hailing platforms (eg, Uber and Didi Chuxing) have become increasingly popular in
recent years. Efficiency has always been an important metric for such platforms. However …
recent years. Efficiency has always been an important metric for such platforms. However …