Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning
There is a growing interest in the optimization of vehicle fleets management in urban
environments. However, limited attention has been paid to the integrated optimization of …
environments. However, limited attention has been paid to the integrated optimization of …
A survey on applications of reinforcement learning in spatial resource allocation
The challenge of spatial resource allocation is pervasive across various domains such as
transportation, industry, and daily life. As the scale of real-world issues continues to expand …
transportation, industry, and daily life. As the scale of real-world issues continues to expand …
Scalable order dispatching through Federated Multi-Agent Deep Reinforcement Learning
Efficient order dispatching is crucial for online ride-hailing systems, directly influencing user
experience and platform revenue. Traditional methods often focus on maximizing immediate …
experience and platform revenue. Traditional methods often focus on maximizing immediate …
Optimal scheduling of shared autonomous electric vehicles with multi-agent reinforcement learning: A MAPPO-based approach
J Tian, H Jia, G Wang, R Wu, H Gao, C Liu - Neurocomputing, 2025 - Elsevier
The advent of shared autonomous electric vehicles (SAEVs) is expected to decarbonize the
transport sector and enable large-scale mobility on demand. Considering the highly …
transport sector and enable large-scale mobility on demand. Considering the highly …
i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance
Ride-hailing platforms have been facing the challenge of balancing demand and supply.
Existing vehicle reposition techniques often treat drivers as homogeneous agents and …
Existing vehicle reposition techniques often treat drivers as homogeneous agents and …
RSTR: A Two-Layer Taxi Repositioning Strategy Using Multi-Agent Reinforcement Learning
H Yu, X Guo, X Luo, Z Wu, J Zhao - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Ride-hailing platforms allow people to request rides, but sometimes they do not respond
quickly enough. Due to the complex urban transportation system and the large geographical …
quickly enough. Due to the complex urban transportation system and the large geographical …
A dynamic region-division based pricing strategy in ride-hailing
B Shi, Y Lu, Z Cao - Applied Intelligence, 2024 - Springer
In recent years, ride-hailing has played an important role in daily transportation. In the ride-
hailing system, how to set the prices for orders is a crucial issue. Considering the variations …
hailing system, how to set the prices for orders is a crucial issue. Considering the variations …
M2MTR: Reposition Idle Taxis in the Many-to-Many Manner with Multi-agent Reinforcement Learning
H Yu, X Guo, J Chen, X Luo - … Conference on Advanced Data Mining and …, 2023 - Springer
Ride-hailing apps, such as Didi and Uber, allow people to easily request a ride by inputting
their desired origin and destination locations. Due to transportation system complexity and …
their desired origin and destination locations. Due to transportation system complexity and …
A Universal Simulation Platform for the Mobility on Demand System
Y Jiang, W Xu, Y Dong - 2024 IEEE 4th International …, 2024 - ieeexplore.ieee.org
Ride-hailing services are typical on-demand mobility services. The efficiency of a ride-
hailing system depends on the degree of coordination between the supply and demand …
hailing system depends on the degree of coordination between the supply and demand …
Multi-agent Reinforcement Learning for Taxi-Fleet Cruising Strategy in Ride-Hailing Services
Y Zhu, W Guo, Z Hua, L Zhang, D Li, W Li - International Conference on …, 2024 - Springer
This study addresses the inefficiencies in how idle taxis determine their cruising strategy,
which currently rely heavily on drivers' personal experiences. Such reliance often leads to …
which currently rely heavily on drivers' personal experiences. Such reliance often leads to …