Imagine a city where your bus, scooter, and train all talk to each other in real-time to avoid jams. It's like having a giant, living digital map that predicts traffic and suggests the best moves for every operator. This helps everyone get from door to door without the usual waiting or congestion.
Cities struggle to manage complex transport systems where e-scooters, on-demand transport, and public transit don't communicate. This leads to fragmented data, high congestion, and long waiting times for passengers.
A multi-layered Digital Twin of urban mobility with AI-powered plug-in modules for traffic monitoring, forecasting, and decision-making.
If you are a transit authority dealing with fragmented data between buses and trains — this project developed a digital twin that improves public transport service levels. It allows for better coordination across different modes of travel to reduce passenger waiting times.
If you are a mobility provider dealing with unpredictable demand and city congestion — this project developed AI-based forecasting tools that help you integrate with other transport modes. This ensures a more seamless journey for users switching from scooters to trains.
If you are a delivery firm dealing with urban traffic bottlenecks — this project developed a secure data sharing system that reduces network congestion. This helps e-cargo bikes and vans find the most efficient routes in real-time.
Based on available project data, no specific pricing or cost details are provided as this is a research and innovation action.
The system is designed as a generic tool and is being validated across 4 different cities: Athens, Helsinki, Amsterdam, and Luxembourg, suggesting a focus on scalability across various urban forms.
Based on available project data, the specific licensing terms are not mentioned, but the project involves a consortium of 22 partners including 11 industry members.
The project uses a privacy-preserving, decentralized data framework and federated learning to ensure secure information sharing between providers.
The project period runs from 2023-06-01 to 2026-05-31, indicating the development and pilot phase concludes in May 2026.
The consortium is heavily weighted toward commercial application, with a 50% industry ratio (11 industrial partners out of 22). The presence of 5 SMEs and 6 universities indicates a balanced pipeline from academic research to small-scale agile development and large-scale industrial implementation across 10 European countries.
Aalto University (AALTO KORKEAKOULUSAATIO SR)
Contact us to connect with the ACUMEN consortium for pilot data or licensing inquiries.
