Shaping the future of Artificial Intelligence (AI) in Mobility

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With more than 150 participants at the AI & Mobility Day, topics discussed include new mobility services; crowd- and traffic management; automated driving; freight mobility; and ethics, data and human autonomy.

To address challenges at the intersection of AI & Mobility, the TU Delft | AI Initiative, Mobility Innovation Centre Delft (MICD) and the Transport & Mobility Institute (TMI) brought together leading experts from science, industry and government during TU Delft's festive Dies Natalis week “Making Sense of Mobility”. Hosted by Mondai | House of AI, the AI & Mobility Day on January 17th, was about shaping the future of AI in mobility.

Expectations for AI in mobility are immense—but will it be the game-changer we hope for, or just another overpromised technology struggling with the complexities of sustainable, resilient, and inclusive transport?
Serge Hoogendoorn, Distinguished Professor Smart Urban Mobility

Challenges for AI in Mobility

The adoption of AI in mobility has been slow, limiting its potential to address the many pressing societal challenges in the field.
Achieving sustainable, resilient, inclusive, healthy, and safe mobility is complex.
Data limitations and the complexity of mobility systems present challenges for effective AI implementation.
Overall, AI's transformative impact on mobility depends on overcoming data limitations, integrating domain expertise, addressing ethical challenges, and fostering collaboration among stakeholders

As I often hear that AI is a one-size-fits-all solution, I like to say that it’s not. Context, specifics, and responsibility matter. To use AI effectively, it is crucial to know which instrument to choose, when to use it, and how to apply it.
Geert-Jan Houben, Pro Vice Rector Magnificus AI, Data and Digitalisation

Opportunities for AI in Mobility

AI offers huge potential for a sustainable, resilient, and fair mobility system.
Opportunities exist in the ‘vertical application’ of AI.
AI can significantly enhance traffic management through smart technologies and data-driven insights.

Enthusiasm for AI is the fastest way of getting some desperately needed domain knowledge transferred from academia to practice
Hans van Lint, Professor of Traffic Simulation and Computing at the Faculty of Civil Engineering and Geosciences

Important takeaways for AI in Mobility

Integration of AI with domain knowledge and existing systems is crucial for successful deployment.
Meaningful human control should be prioritised in the development and implementation of automated driving.
Ethical concerns, including bias, privacy, and human autonomy, must be carefully addressed.
Freight mobility needs a digital transformation, incorporating AI-driven analytics for optimisation.
Responsible AI development and deployment require a human-cantered perspective, addressing ethical considerations and potential biases.
A collaborative learning community is vital for exploring AI's potential in mobility while mitigating risks.

Crises reshape decision-making and sensemaking, highlighting the need for AI to be deeply contextualised to provide meaningful and effective solutions.
Tina Comes, Professor Decision Theory & ICT for Resilience at the Faculty of Technology, Policy & Management

What did you miss at the AI & Mobility Day?

CHECK OUT EVENT SLIDES OF THE MORNING AND AFTERNOON PROGRAMME

Challenge and question of the day

Serge Hoogendoorn (TU Delft Transport & Mobility Institute), Sascha Hoogendoorn-Lanser (Mobility Innovation Centre Delft), Geert-Jan Houben (TU Delft AI Initiative), and Inald Lagendijk (AIC4NL) opened the event by introducing the theme of the day and presenting interesting examples of working areas within AI & Mobility.

The main challenge of the day was "How do we tackle the wicked problem of designing, planning and operating a sustainable, resilient and fair mobility system?", and the main question of the day was "Is AI the 'Swiss Army Knife' for solving the wicked problem of designing, planning and operating a sustainable and resilient mobility system?"

Crowd management and automated driving

Marco Hennipman (Siemens Mobility) and Emir Demirović (Faculty of EEMCS, XAIT Lab) presented how AI can be used for novel mobility service, and discussed evidence-based trust in AI. Mark Hünneman (SAIL), Eelco Thiellier (SAIL), and Yanan Xin (Faculty of CEG, DAIMoND Lab) discussed crowd management (and monitoring), the risks, planning and the role of AI, and how it can be applied for events and in the city of Amsterdam.

The event continued with an engaging panel discussion featuring Holger Caesar (Faculty of ME, ELLIS), Edwin Nas (RDW), Arkady Zgonnikov (Faculty of ME, HERALD Lab) and Simeon Calvert (Faculty of CEG, CityAI Lab), with Sascha Hoogendoorn-Lanser as moderator. It began with an explanation of the principles of automated driving, followed by a lively debate. Edwin raised the question of whether AI driving performance could be properly evaluated, noting that AI-driven vehicles are currently prohibited in the EU. Holger suggested that AI systems should undergo a test similar to the driving test required for civilians. This sparked audience questions, such as whether the test should mirror civilian standards and who should define driving styles, for example a 20-year-old versus a 60-year-old. The panel agreed that AI evaluation might need a distinct approach, with driving instructors playing a key role in defining the standards. They also highlighted variations in driving behaviour across countries. Arkady observed that regulators are still far from establishing rules for AI in driving, as they lack a full understanding of the technology.

Any vehicle that makes autonomous driving decisions on behalf of the driver (such as lane changes or turns, but excluding lane keeping and collision avoidance) must demonstrate safety through government-mandated tests before being deployed on the road.
Holger Caesar, Assistant professor at Faculty of Mechanical Engineering & Board Member of ELLIS Delft Unit

AI safety and traffic management

The event continued with a presentation by Robert Jan ter Kuile and Nathalie Niessen of Deloitte, discussing the future of mobility accelerated by AI. They presented on AI safety and critical regulations, especially in mobility, emphasising the importance of high-quality data for training AI models effectively.

Alongside the lunch break, a poster and demo market flourished featuring multiple posters showcasing PhD research from the CityAI Lab, DAIMoND Lab, HERALD Lab, RAIL Lab (ICAI) and XAIT Lab. Alongside, several live demos by companies Argaleo and Arane, and TU Delft AI Labs.

AI and traffic management were the focus of the second panel, with Sascha Hoogendoorn-Lanser as moderator. Marco Schreuder (RVS, WVL) started with highlighting current challenges in road traffic management. Olaf Vroom (NDW) proposed that in modern traffic management, the key to progress is not just more data, but smarter ways of transforming, interpreting, and applying it to predict, respond, and adapt in real time. Sebastian Schwinn (d-fine) followed by discussing new insights from application of AI for tunnel safety management. Hans van Lint (TU Delft Faculty of CEG) concluded with a discussion on the need for integration of AI and domain knowledge.

Thereafter, Neil Yorke-Smith (Faculty of EEMCS) and Mahnam Saeednia (Faculty of CEG) challenged the audience to rethink freight mobility in the digital era.

Gerard Kuijlaars (Police Netherlands) and Tina Comes (Faculty of TPM) concluded the day by stressing the importance of ethics and human autonomy in AI-driven decision making, while also highlighting that in case of a crisis, decision-making and sensemaking changes – AI needs to be contextualised.

The success of AI in traffic management depends not only on technical innovation but on its ability to integrate with existing systems, improve real-time decision-making, and scale from operational fixes to strategic planning.
Marco Schreuder, Senior Advisor and Coordinator Active Traffic Management at Rijkswaterstaat

Photos AI & Mobility Day 2025

Get involved with AI & Mobility

Join us as TU Delft in tackling these challenges and more at the intersection of AI and Mobility. Get inspired by the work from our many teams and labs working on these topics: 3DUU Lab, CityAI Lab, DAIMoND Lab, HERALD Lab, RAIL Lab, Sensor AI Lab, XAIT Lab

Are you interested in partnering up with one of these or our other TU Delft teams working on the future of AI in mobility? Get in contact with the Mobility Innovation Centre Delft (MICD) via Sascha Hoogendoorn-Lanser or Mondai | House of AI, where TU Delft connects and enables all stakeholders in the development of AI.