Introduction
Centralized eco-driving systems exploit vehicle-to-infrastructure (V2I) communication to coordinate vehicle speed profiles based on traffic signal and network information. By enabling predictive and cooperative driving, it reduces energy consumption, emissions, and travel time, while improving traffic flow efficiency and driving comfort.
Goals
This thesis proposes the development of a smart traffic light control system capable of dynamically managing signal phase durations to optimize traffic flow across multiple interconnected intersections. The proposed controller aims to reduce congestion, travel time, and stop-and-go behaviour by adapting signal timings in real time based on traffic demand. Unlike traditional fixed-time or isolated adaptive controllers, the system will consider network-level coordination, enabling intersections to cooperate and jointly optimize overall traffic performance.
Requirements include:
- Familiarity with Matlab or Python;
- in optimal control theory;
- Basic knowledge of ROS or ROS2;
- Additional background in data analysis, simulation, or SUMO is a plus (can be learned during the project).
Contact
Daniele Vignarca: daniele.vignarca@polimi.it
Stefano Arrigoni: stefano.arrigoni@polimi.it
For inquiries and further information, please email the first author, copying the other authors in CC