Mr. Rose, what does CAPTN Flex do?

CAPTN Flex is our Smart City project. Here, AI methods and architectures are used for data collection as well as for efficient processing and analysis to improve mobility predictions and establish a flexible, demand-driven public transportation system. To achieve this, user data must be collected. Understanding the conditions under which people are willing to share their data is also part of CAPTN Flex. Malten Rose explains what this subproject looks like.

CAPTN: Please introduce yourself.

My name is Malten Rose. I am a research associate at the Chair of Technology Management, led by Prof. Dr. Carsten Schultz, here at Kiel University, and I also supervise the CAPTN Flex project.

What is CAPTN Flex about?

CAPTN Flex is a smart mobility project that aims to make traffic flows more flexible. This means that a system should be able to respond to customer demands in a needs-based manner. This requires an enormous amount of data from various modes of transport, which must be processed and integrated in a user-oriented way to enable the most efficient traffic flow possible.

What does this flexibility look like?

Public transportation in the future will no longer strictly follow fixed routes but will instead respond flexibly to precisely calculated demands. Here’s an example: In a particular neighborhood, 100 people live there, and every morning at 8 a.m., nine of them need to get to school. These are fixed data points that are relatively easy to determine. Three people from the neighborhood have a doctor’s appointment at 10 a.m.; five others meet friends in cafés in the old town. These are individual needs that are harder to predict and do not occur regularly. They must be derived from movement profiles and passenger data. To pick someone up conveniently at their doorstep and get them to their destination on time, additional user and real-time traffic data must be integrated.

But that’s not possible with public transport as it exists today.

Exactly. Today, bus schedules are fixed, dictating when a bus should be at a particular stop. The core idea of CAPTN Flex is that users know when and where they need to be to catch a ride to their destination. Perhaps through an app, they can enter their destination and receive information about when they will be picked up—maybe by a shared taxi—along with the planned route and expected arrival time. The app could also show bike-sharing options and whether an alternative mode of transport might get them there even faster. These are new concepts for how urban mobility could function in the future. However, they require an enormous amount of data. That is the major challenge we face: being able to collect and process these data to create a mobility system with clear added value.

Are you already considering autonomous transport solutions?

We view autonomous vehicles as a separate component. They are not explicitly part of our project, but they are, of course, a valuable addition. After all, finding drivers for buses, ferries, or shared taxis is increasingly difficult. This is a key driver for autonomous vehicles. Additionally, future mobility might require smaller transportation units, which would mean needing even more drivers—something that is unlikely to be feasible. Therefore, it seems inevitable that autonomous transport will play a role in flexible systems. Or we might see a mix of automated and conventional transport solutions operating in parallel. I see tremendous potential and demand for flexible and autonomous transport solutions, particularly in rural areas. The connectivity problem is much more severe there compared to cities, where trams, bike-sharing, scooters, car-sharing, and buses are readily available. That’s why I believe we might see the implementation of such solutions in rural areas even before they reach urban centers.

But you are not directly focused on traffic flow, correct?

Two research groups are involved in CAPTN Flex: the technical aspects are handled by the Chair of Reliable Systems, led by Prof. Dr. Dirk Nowotka, while our focus is more on organizational issues—specifically on user acceptance. Flexible systems require a significant amount of personal data to provide the best possible service. Some of this data is highly sensitive. We are trying to understand how to increase acceptance for sharing such data.

How are you approaching this topic?

We recently conducted a study to examine the impact of an external data breach on our project. We analyzed whether geographical distance makes a difference—does it matter if data is intercepted locally or in another country? We also investigated the extent to which data protection labels influence people’s willingness to share their data. In an earlier study, my colleagues conducted a survey on the innovation capabilities of municipalities, because visionary ideas like flexible traffic flows can only succeed if public authorities are willing to support them.

So, you’re analyzing how users feel about sharing their data?

Exactly. I am trying to determine the threshold where the perceived benefits for customers are high enough while the perceived risks remain low. The goal is not to collect unnecessary data but only the data required to ensure the best possible service.

Do people react differently depending on where their data is stored?

Yes. We found that in many cases, people are more willing to share their data if it is stored on municipal servers rather than with Google, Amazon, or Meta in the U.S. Another important factor is data ownership—who owns the data and who has access to it? Data stored cooperatively, where users have access and control, is perceived as particularly trustworthy. So, storage location has a significant impact on acceptance.

Can the findings already be implemented?

The project runs until the end of this year. By then, we likely won’t have real, usable data collected. So, for now, it remains theoretical—a kind of foundational research for introducing such systems. However, implementation is not our role as a university; our job is research. In practice, various stakeholders, such as KielRegion and NahSH, are already working on similar concepts. We are in close communication with them.

Finally, where do you see the greatest potential for this project in the future?

I see enormous potential for a flexible and potentially autonomous transport system to make public transit more resource-efficient, effective, and economical. My hope is that we find ways to increase user acceptance of a data-driven, flexible system.

(Translated with assistance of ChatGPT)