Imagine a city where a digital conductor manages every car and bus like an orchestra to prevent traffic jams. Instead of drivers guessing the best route, a smart system balances the load across the city in real-time. It blends self-driving cars with regular traffic to make sure people and packages get where they need to go without the usual gridlock.
City transport is currently fragmented, leading to congestion, pollution, and inefficient movement of goods and people. Existing systems cannot effectively coordinate a mix of human-driven and autonomous vehicles.
A common open platform featuring multi-agent reinforcement learning traffic controllers and simulation tools for fleet and traffic management.
If you are a delivery provider dealing with urban congestion and inefficient routing — this project developed urban logistics models (UC3) that optimize the transport of goods. This allows for more predictable delivery windows and lower fuel costs.
If you are a transport authority dealing with rigid bus schedules and passenger overcrowding — this project developed demand-response transport tools (UC2) that adjust fleet movement based on real-time needs. This increases vehicle occupancy and reduces empty runs.
If you are a software developer dealing with the difficulty of integrating self-driving cars into human-driven traffic — this project developed a cooperative traffic management system using machine learning. This ensures autonomous vehicles can be safely controlled at a high level within city networks.
Based on available project data, no specific commercial pricing or cost per license is mentioned; the project was funded by an EU contribution of EUR 4,598,550.
Yes, the project focused on high-level traffic and fleet management for future cities, validating its models through three use cases and five pilots.
Based on available project data, the innovations are integrated into a common, open platform, though specific licensing terms for commercial use are not detailed.
The system is designed for interoperability, combining signal control with dynamic bus-lane management and data fusion to work with both automated and conventional vehicles.
The project development period runs from 2022-11-01 to 2025-10-31.
The consortium is heavily industry-weighted with 9 industrial partners (56% ratio), including 7 SMEs. This suggests a strong focus on commercial viability rather than pure academic research, with a diverse geographical spread across 7 European countries.
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