5 papers
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 Explainable Anomaly Detection in Shared Mobility Systems
Elnur Isgandarov, Matteo Cederle, Federico Chiariotti +1
Shared mobility systems, such as bike-sharing networks, play a crucial role in urban transportation. Identifying anomalies in these systems is essential for optimizing operations,…
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…
VoI-aware Scheduling Schemes for Multi-Agent Formation Control
Federico Chiariotti, Marco Fabris
Formation control allows agents to maintain geometric patterns using local information, but most existing methods assume ideal communication. This paper introduces a goal-oriented…
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…