Modern urban mobility faces unprecedented challenges as cities struggle to manage increasingly complex multimodal transport systems while meeting ambitious sustainability, safety and efficiency goals. The emergence of new mobility services like on-demand transport, Mobility-as-a-Service (MaaS), e-scooters, and e-cargo bikes, is fundamentally disrupting how citizens and businesses move, making traditional traffic management approaches increasingly inadequate. Despite advances in Machine Learning, AI, and Connected Mobility technologies, their full potential remains untapped due to fragmented implementation, lack of coordination between modes and operators, and absence of frameworks for data exchange and interoperable decision-making.
ACUMEN is transforming this landscape by developing a generic, privacy-preserving, data-driven digital paradigm for advanced network management. The project's vision is to support the transition to seamless, sustainable, connected and automated mobility through three key innovations: (1) designing a secure, decentralised data framework enabling real-time information sharing between mobility providers; (2) leveraging explainable AI and hybrid intelligence for unprecedented accuracy in monitoring and forecasting; and (3) developing novel decision-making solutions that foster cooperation between mobility providers across all urban scales.
These innovations are being integrated into a digital twin environment, offering stakeholders a unified view of transport systems to better handle both daily operations and long-term evolution. The project is demonstrating impact through four complementary pilots in Athens, Helsinki, Amsterdam and Luxembourg, each testing different aspects of the system under real conditions. Expected impacts include reduction in waiting times for multimodal trips, dea crease in network congestion, and improvement in public transport level of service.
ACUMEN takes an interdisciplinary approach, combining technical expertise in transport engineering, computer science and AI with social sciences perspectives on user behaviour, policy frameworks and governance models. This integration ensures the developed solutions address not just technical challenges but also social, economic and institutional barriers to adoption.