07 May 2025

The ACUMEN project is developing a governance and regulatory framework, as well as a decision support tool, to guide policy makers in integrating artificial intelligence (AI) and machine learning (ML) into transport management systems. To ensure practical relevance, ACUMEN conducted four workshops with its pilot cities – Luxembourg, Athens, Helsinki and Amsterdam – bringing together national and local traffic management stakeholders.

These workshops provided an insight into existing traffic management systems and facilitated the exchange of best practices. Feedback from participants will be used to refine the governance framework and decision-making tool to ensure its applicability in real transport systems. The final tool will be shared with stakeholders to demonstrate the benefits of AI-based traffic management and encourage cities to adopt AI-based solutions for long-term mobility improvements.

The Helsinki workshop brought together representatives from Traficom, PayiQ, the City of Helsinki, Forum Virium Helsinki, Aalto University and Fintraffic to explore the effective integration of AI in traffic management. Discussions covered governance, regulatory challenges and implementation strategies to ensure the responsible use of AI in transport.

The ACUMEN pilot in in Helsinki aims to promote sustainable transport choices and improve the overall travel experience for both ferry passengers and local travellers in the south-eastern Jätkäsaari region. the pilot will identify the requirements and interfaces to link the ACUMEN digital twin architecture, allowing for seamless integration with analytics and AI-powered seamless integrated multimodal traffic management tools. In addition, it will use traffic simulation, taking into account last-mile logistics and other factors as well as drone-collected data. This data will be used for mobility analyses during different stages of the intervention and for calibrating the simulation. Test users will be offered alternative modes or routes via a traffic-related app. The pilot project will focus on testing the impact of soft measures on network-level traffic flows in different intervention phases, taking into account input from end-users, citizens and mobility-related societal considerations, including climate neutrality.

Participants agreed that AI has significant potential to improve traffic management systems by optimising traffic flow, improving road safety, reducing emissions and supporting data-driven decision making. However, key barriers remain, including regulatory uncertainty, public scepticism, privacy concerns, and the complex roles of public and private stakeholders.

Regulatory frameworks, such as the AI Act and the GDPR, impose strict compliance requirements that can hinder the adoption of AI in transport. Finland’s LiPa Act poses additional challenges, highlighting the need for a clear, standardised legal framework to support AI integration. Data quality and accessibility also emerged as critical issues, with inconsistent updates on road works, incomplete traffic data and legal restrictions on data sharing limiting the effectiveness of AI-driven solutions.

Potential solutions discussed included:

• Introduce AI-driven data fusion tools to improve decision-making and operational efficiency.
• Establish public-private partnerships for enhanced data sharing and access.
• Implement standardised data collection protocols to boost accuracy and interoperability.
• Promote multi-stakeholder engagement as essential for effective AI governance.
• Foster collaboration between government agencies, technology providers, public transport operators, and citizens to ensure compliance, transparency, and trust.
• Develop collaborative governance models to manage AI integration responsibly.
• Create harmonised regulations and ethical AI guidelines for transport management.