5 papers · 1 filter
Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
Edoardo Scarpel, Alberto Pettena, Matteo Cederle +3
This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations of periodic, system-wide inte…
Towards Fair and Efficient allocation of Mobility-on-Demand resources through a Karma Economy
Matteo Cederle, Saverio Bolognani, Gian Antonio Susto
Mobility-on-demand systems like ride-hailing have transformed urban transportation, but they have also exacerbated socio-economic inequalities in access to these services, also due…
A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management
Matteo Cederle, Marco Fabris, Gian Antonio Susto
This study introduces a novel multi-objective reinforcement learning (MORL) approach for autonomous intersection management, aiming to balance traffic efficiency and environmental…
Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning
Matteo Cederle, Marco Fabris, Gian Antonio Susto
In the growing field of Shared Micromobility Systems, which holds great potential for shaping urban transportation, fairness-oriented approaches remain largely unexplored. This wor…
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
Matteo Cederle, Luca Vittorio Piron, Marina Ceccon +4
As Machine Learning grows in popularity across various fields, equity has become a key focus for the AI community. However, fairness-oriented approaches are still underexplored in…