Objective 1

Design and implement a secure, data-driven, decentralised data framework.

Objective 2

Propose and evaluate an actionable Hybrid Intelligence (HI) forecasting and simulation framework, based on advanced Machine Learning (ML)/ Artificial Intelligence (AI) models and incorporating human intelligence

Objective 3

Develop and evaluate a trusted ACUMEN management framework.

Objective 4

Develop and evaluate a novel mixed decision paradigm combining local decentralised decision-making and a centralised orchestrator using AI-based proxy models, enabling secure negotiation between multiple stakeholders, and aligning individual performance targets with overarching multimodal objectives without full knowledge of stakeholder intentions.

Objective 5

Develop a generic and trusted ACUMEN Digital Twin Integrator for any digital service, model, and AI-enabled management strategy.

Objective 6

Demonstrate the effectiveness of the innovative methods and tools developed in 1-4 for data analysis, HI forecasting and AI-assisted decision-making through a series of pilots in Athens, Helsinki, Amsterdam and Luxembourg, covering different operational scales, mobility needs, patterns, modes, services and data availability.

Objective 7

Develop a comprehensive impact assessment framework to assess existing systems and evaluate the novel trusted AI-enabled ACUMEN solutions.

Objective 8

Disseminate and scale up developed solutions and results to support EU stakeholders in the deployment of next-generation multimodal network and traffic management services.