Deep reinforcement learning with knowledge transfer for online rides order dispatching
Ride dispatching is a central operation task on a ride-sharing platform to continuously match
drivers to trip-requesting passengers. In this work, we model the ride dispatching problem as …
drivers to trip-requesting passengers. In this work, we model the ride dispatching problem as …
Optimizing large-scale fleet management on a road network using multi-agent deep reinforcement learning with graph neural network
J Kim, K Kim - 2021 IEEE International Intelligent …, 2021 - ieeexplore.ieee.org
We propose a novel approach to optimize fleet management by combining multi-agent
reinforcement learning with graph neural network. To provide ride-hailing service, one …
reinforcement learning with graph neural network. To provide ride-hailing service, one …
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 …
Multi-objective distributional reinforcement learning for large-scale order dispatching
The aim of this paper is to develop a multi-objective distributional reinforcement learning
framework for improving order dispatching on large-scale ride-hailing platforms. Compared …
framework for improving order dispatching on large-scale ride-hailing platforms. Compared …
Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach
In this paper, we define and investigate a novel model-free deep reinforcement learning
framework to solve the taxi dispatch problem. The framework can be used to redistribute …
framework to solve the taxi dispatch problem. The framework can be used to redistribute …
Efficient large-scale fleet management via multi-agent deep reinforcement learning
Large-scale online ride-sharing platforms have substantially transformed our lives by
reallocating transportation resources to alleviate traffic congestion and promote …
reallocating transportation resources to alleviate traffic congestion and promote …
Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
The vehicle dispatching system is one of the most critical problems in online ride-hailing
platforms, which requires adapting the operation and management strategy to the dynamics …
platforms, which requires adapting the operation and management strategy to the dynamics …
Coride: joint order dispatching and fleet management for multi-scale ride-hailing platforms
How to optimally dispatch orders to vehicles and how to trade off between immediate and
future returns are fundamental questions for a typical ride-hailing platform. We model ride …
future returns are fundamental questions for a typical ride-hailing platform. We model ride …
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 …
Good or mediocre? A deep reinforcement learning approach for taxi revenue efficiency optimization
Recently, with the rapid expansion of cities, optimizing taxi driving routes for improving taxi
revenue efficiency has become the core issue of taxi system. However, most current …
revenue efficiency has become the core issue of taxi system. However, most current …