About

Cities are growing fast while urban space is scarce. At the same time, new mobility services and digital technologies are evolving rapidly. Many public and private actors, each with their own goals, shape the design and management of mobility and logistics. Without careful coordination, this can lead to conflicting interests and outcomes that are socially undesirable or inequitable. The Mobility DesAIgn Lab addresses this complexity by advancing AI‑enabled methods that help stakeholders co‑create mobility systems that are fair, effective, and future‑proof.

From predictive to prescriptive digital twins

Traditional planning tools rely on predictive digital twins built on traffic and transport models. Stakeholders propose a handful of options, and models estimate impacts on accessibility, network performance, and other indicators. This approach has two big limitations: only a small set of designs can be explored, and a small group of decision‑makers tends to dominate the process, raising the risk of unintentional bias.

Our lab focuses on prescriptive digital twins, which can generate and recommend design alternatives themselves. These tools can surface a far larger, more diverse solution set and ensure that all population groups are considered by explicitly incorporating stakeholder preferences and equity indicators into the objectives.

Public authorities and area developers are eager to use prescriptive digital twins to accelerate decisions on mobility and spatial design. Because these systems learn from user input and provide policy recommendations, they qualify as AI systems under the European AI Act and must be used in a compliant and responsible manner. Decisions taken with these tools can influence people’s ability to access essential services at scale; that makes careful risk assessment, transparency, and human oversight essential.

Integrating AI into digital twins raises important ethical, legal, and societal questions. For example:

• Bias in recommendations could limit access to new services for marginalised communities.
• Traffic management strategies must avoid systematically disadvantaging e.g. lower‑income neighbourhoods.
• Mobility hub placement could be skewed toward affluent areas if equity is not explicitly considered.
• Shared mobility operations may concentrate supply in high‑demand districts, neglecting others.

Focus of the Mobility DesAIgn Lab

We investigate the aforementioned implications and translate them into practical guidance so that cities and developers can start using prescriptive digital twins responsibly and in line with the AI Act. Our scope spans key design and management questions: strategic planning for road, public transport, cycling and parking; mobility and logistics hubs; demand‑responsive and shared services; and mobility, traffic, incident, and asset management.

Our research and experimentation are organised around three concrete use cases:

• Sustainable mobility - locations for hubs, integration of shared services, and public transport improvements
• Car‑restrictive interventions - parking and pricing measures that support modal shift.
• Future traffic management - strategies for networks with connected and automated vehicles.

Each year, we run interactive design sessions that bring together municipalities, agencies, companies, researchers, and citizens. These sessions speed up decision‑making, reveal how AI behaves in real planning contexts, and show what is acceptable and effective for sustainable urban well‑being. The insights feed into recommendations for responsible deployment today and into proposals to refine AI regulations.