Mobility system design with AI-based digital twins

Challenge:
The global urbanization and growth of cities lead to a significant increase in mobility, logistics and housing demand, while the available space remains scarce. There is a pressing need for more advanced design methodologies that are capable of dealing with increasingly complex design problems. Artificial intelligence (AI) can be a crucial instrument to assist the stakeholders in choosing suitable designs and management strategies in a complex urban environment.

Our solution:
In the Mobility DesAIgn Lab we focus on novel ‘prescriptive’ digital twins based on AI and optimisation techniques. Unlike predictive digital twins, which only provide insights into the expected impact of interventions, prescriptive digital twins can generate and propose designs independently. Prescriptive digital twins learn from user inputs and provide recommendations to assist policymakers. These methods have the potential to address the challenges associated with traditional approaches:

• Generate innovative designs

• Enhanced stakeholder engagement

• Faster and streamlined decision-making process

• Reduced investment costs

Prescriptive digital twins are classified as AI systems under Article 3, point 1 of the AI Act, making compliance with the AI Act mandatory.

Impact:

• The societal impact includes creating fair and accessible mobility systems that enhance urban liveability and safety.

• The economic impact results from optimising design processes, reducing costs and planning time, which benefits stakeholders economically. Enhanced stakeholder engagement leads to more effective mobility solutions, fostering a better business environment and attracting investments.