Dynamic fleet management with rewriting deep reinforcement learning
W Zhang, Q Wang, J Li, C Xu - IEEE Access, 2020 - ieeexplore.ieee.org
Inefficient supply-demand matching makes the fleet management a research hotpot in ride-
sharing platforms. With the booming of mobile network services, it is promising to abate the …
sharing platforms. With the booming of mobile network services, it is promising to abate the …
Promoting Collaborative Dispatching in the Ride-Sourcing Market With a Third-Party Integrator
Y Wang, J Wu, H Sun, Y Lv… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The integrated ride-sourcing mode, developed by third-party integrators, is a feasible
solution to market fragmentation because it integrates travel demand and vehicle supply …
solution to market fragmentation because it integrates travel demand and vehicle supply …
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 …
Supply-demand-aware deep reinforcement learning for dynamic fleet management
Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …
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 …
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 …
An order dispatch system based on reinforcement learning for ride sharing services
Z Chen, P Li, J Xiao, L Nie, Y Liu - 2020 IEEE 22nd International …, 2020 - ieeexplore.ieee.org
Ride-sharing has been widely used in many cities, such as Didi and Uber. Ride-sharing is
regarded as an effective way to solve urban traffic congestion and pollution. However, most …
regarded as an effective way to solve urban traffic congestion and pollution. However, most …
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 …
Vehicle dispatching and routing of on-demand intercity ride-pooling services: A multi-agent hierarchical reinforcement learning approach
The integrated development of city clusters has given rise to an increasing demand for
intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading …
intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading …
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 …