Imagine if cars and delivery vans could talk to the road and each other to prevent traffic jams before they happen. This work creates a digital brain for cities that coordinates self-driving vehicles and public transport in real-time. It's like a high-tech air traffic control system, but for city streets, making trips safer and faster for everyone.
Current city traffic systems cannot efficiently manage the mix of human-driven and automated vehicles, leading to congestion and safety risks. There is a lack of interoperability between different fleet management platforms and city infrastructure.
A reference architecture for a CCAM platform, AI-based optimal control algorithms using Deep Reinforcement Learning, and intermodal interfaces for shared mobility and goods delivery.
If you are a fleet operator dealing with urban congestion and delivery delays — this project developed goods management and delivery interfaces that optimize flow. This allows for smoother routing and reduced idling in city centers.
If you are a city authority dealing with road accidents and emissions — this project developed AI-based traffic optimization and flow balancing. It uses real-time data to adjust green lights and reduce congestion.
If you are a platform provider dealing with fragmented transport modes — this project developed intermodal interfaces for shared mobility. This enables a seamless transition between different automated transport services.
Based on available project data, no specific pricing or commercial cost models are provided, although the project aims to develop new business models for operating these services.
The project tested solutions in 4 Lead Living Labs (Tampere, Trikala, Turin, Vigo) and 2 Follower Living Labs (Bari, Quadrilatero), suggesting a scalable approach across different geographic locations.
Based on available project data, specific IP or licensing terms are not mentioned, but the project involves a consortium of 21 partners including 6 industry members.
The system integrates into existing Intelligent Transport Systems by defining intermodal interfaces for network load balancing and shared mobility.
The project period runs from 2022-11-01 to 2025-10-31, indicating that the final results and validations are expected by late 2025.
The consortium is well-balanced for technology transfer, featuring 21 partners across 10 countries. With an industry ratio of 29% (6 industrial partners, including 4 SMEs), there is a strong bridge between the 8 academic/research entities and commercial application. The presence of 7 'Other' entities likely represents the municipal authorities managing the Living Labs, ensuring the technology is tested in real urban environments.
Contact Politecnico di Bari regarding the CCAM platform reference architecture.
Contact us to explore licensing opportunities for the AI-based traffic control algorithms.
