Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning

W Tu, H Ye, K Mai, M Zhou, J Jiang… - International Journal …, 2024 - Taylor & Francis
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 …

A survey on applications of reinforcement learning in spatial resource allocation

D Zhang, M Wang, J Mango, X Li, X Xu - Computational Urban Science, 2024 - Springer
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 …

Scalable order dispatching through Federated Multi-Agent Deep Reinforcement Learning

Y Jing, B Guo, N Li, Y Ding, Y Liu, Z Yu - Expert Systems with Applications, 2025 - Elsevier
Efficient order dispatching is crucial for online ride-hailing systems, directly influencing user
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 …

i-Rebalance: Personalized Vehicle Repositioning for Supply Demand Balance

H Chen, P Sun, Q Song, W Wang, W Wu… - Proceedings of the …, 2024 - ojs.aaai.org
Ride-hailing platforms have been facing the challenge of balancing demand and supply.
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 …

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 …

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 …

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 …

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 …