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3 Questions for

Tobias Hesse

Acting Director of the Road System Technology Institute at the German Aerospace Centre (DLR)

Picture of Tobias Hesse.

"Without clear objectives, AI will remain ineffective in traffic management."

Local authorities face a multitude of challenges when it comes to mobility – reducing congestion, cutting emissions and improving road safety. Artificial intelligence (AI) can help to manage traffic more effectively, use infrastructure more efficiently and better coordinate mobility services. This opens up opportunities for local authorities and cities to increase sustainability, efficiency and quality of life. In this interview, Tobias Hesse explains what it takes to successfully design AI-based traffic management and why clearly defined goals and measurable indicators are crucial to this. He is Acting Director of the Road System Technology Institute at the German Aerospace Centre and a member of Plattform Lernende Systeme.

1

Mr Hesse, why is there now a need for a new target vision for AI-based traffic control?

Tobias Hesse: AI is currently creating new opportunities and hopes in many areas at a rapid pace. At the same time, many local authorities lack the resources to build up extensive expertise on their own, develop consistent visions for the future and move beyond isolated experiments in individual projects, such as traffic light systems or forecasts. On the one hand, this means that innovations are only introduced slowly; on the other hand, it creates dependencies on commercial system providers.

The target vision outlined in the policy paper is intended to provide guidance in this regard for local authorities facing very specific decisions, for example with regard to investments, data infrastructures or organisational responsibilities. It presents a structured framework and clearly shows how local authority goals, operational management and innovation can be systematically linked – and why AI then delivers real added value for the climate, security and quality of life.

2

What role do key performance indicators (KPIs) play in AI-based mobility management?

Tobias Hesse: KPIs are the link between politics, administration and technology. They translate social goals – such as emission reduction or accessibility – into measurable variables. Only then can AI learn and control in a targeted manner. Only with clearly defined indicators can a municipality achieve its goals with AI – even with constantly changing traffic situations and participants. At the same time, KPIs create transparency: they make effects visible and enable evidence-based communication with politicians and the public.

3

Why is an open research and development system so crucial?

Tobias Hesse: In my view, there are two key aspects: firstly, an open research and development system ensures that the necessary pace of innovation can be achieved and maintained. Secondly, an open system makes it possible to avoid problematic dependencies and lock-in effects. The mobility sector is extremely dynamic. However, the traditional research, development, tendering and operational process often takes many years before new results and functions are transferred by commercial providers into the operational activities of a local authority. An open R&D system allows local authorities to test new ideas, algorithms or services without jeopardising ongoing operations. This creates a direct link between research and operations, saving many years, automatically aligning research more closely with real needs and giving local authorities greater sovereignty because they decide which innovations to adopt – and which not to.

The discussion paper ‘AI-based traffic and mobility control in municipal mobility systems’ (in German) is available for download here.

The interview is released for editorial use (provided the source is cited © Plattform Lernende Systeme).

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