Lara Codeca

Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning...

On the evaluation of make-before-break handovers in urban WiFi networks for moving vehicles

Improving traffic in urban environments applying the Wardrop equilibrium

A Car Hacking Experiment: When Connectivity Meets Vulnerability

IoT and Microservices Based Testbed for Connected Car Services

SAGA: An Activity-based Multi-modal Mobility ScenarioGenerator for SUMO

DEMO - A Simulation Framework for Multi-modal Commuting and Parking Optimization

Using Deep Reinforcement Learning to Coordinate Multi-Modal Journey Planning with Limited Transportation Capacity

A SUMO-Based Parking Management Framework for Large-Scale Smart Cities Simulations

Towards multimodal mobility simulation of C-ITS: The Monaco SUMO traffic scenario

Luxembourg SUMO Traffic (LuST) Scenario: 24 hours of mobility for vehicular networking research