Reinforcement learning-based order-dispatching optimization in the ride-sourcing service
Y Wang, H Sun, Y Lv, X Chang, J Wu - Computers & Industrial Engineering, 2024 - Elsevier
Since the emergence of ride-sourcing, the dispatching problem has been a hot research
issue in the travel market. Previous studies designed various refined algorithms to improve …
issue in the travel market. Previous studies designed various refined algorithms to improve …
A queueing-theoretic framework for vehicle dispatching in dynamic car-hailing
With the rapid development of smart mobile devices, the car-hailing platforms (eg, Uber or
Lyft) have attracted much attention from both the academia and the industry. In this paper …
Lyft) have attracted much attention from both the academia and the industry. In this paper …
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 …
Fed-LTD: Towards cross-platform ride hailing via federated learning to dispatch
Learning based order dispatching has witnessed tremendous success in ride hailing.
However, the success halts within individual ride hailing platforms because sharing raw …
However, the success halts within individual ride hailing platforms because sharing raw …
Distributed fleet control with maximum entropy deep reinforcement learning
T Oda, Y Tachibana - 2018 - openreview.net
In the context of modern vehicle fleets, such as ride-hailing platforms and taxi companies,
the ability to proactively dispatch vehicles is instrumental in reducing passenger waiting time …
the ability to proactively dispatch vehicles is instrumental in reducing passenger waiting time …
Multiagent Reinforcement Learning‐Based Taxi Predispatching Model to Balance Taxi Supply and Demand
With the improvement of people's living standards, people's demand of traveling by taxi is
increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their …
increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their …
Real-time dispatching of large-scale ride-sharing systems: Integrating optimization, machine learning, and model predictive control
This paper considers the dispatching of large-scale real-time ride-sharing systems to
address congestion issues faced by many cities. The goal is to serve all customers (service …
address congestion issues faced by many cities. The goal is to serve all customers (service …
Effective credit assignment deep policy gradient multi-agent reinforcement learning for vehicle dispatch
X Huang, X Zhang, J Ling, X Cheng - Applied Intelligence, 2023 - Springer
With the emergence of online car-hailing platforms, more travel options and convenience
have been provided to people. However, the'tidal phenomenon'of travel often leads to an …
have been provided to people. However, the'tidal phenomenon'of travel often leads to an …
Scalable deep reinforcement learning for ride-hailing
Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to
meet ride requests throughout the day. We consider a Markov decision process (MDP) …
meet ride requests throughout the day. We consider a Markov decision process (MDP) …
Flexpool: A distributed model-free deep reinforcement learning algorithm for joint passengers and goods transportation
K Manchella, AK Umrawal… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from
last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of …
last-mile deliveries. On the other hand, ride-sharing has been on the rise with the success of …