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/ Reading: 4 min.

21. Mar 2024
/ Germany
Perspectives from Milena Akemann
Milena works as a Technical Product Manager in the Mobility Analytics & Consulting team. She came to ioki through an internship after completing her maths degree. As a mathematician, she has a passion for anything related to optimisation and it was important to her to use her skills to shape the future. She then discovered the area of mobility through a seminar at university on mathematical transport planning.

Milena, what do you particularly like about your job?

What I love most about my job is working with the fantastic team at ioki. Everyone is highly motivated, always gives their best, challenges the status quo and tries to improve things. My work is also incredibly varied and there are always completely new topics that you can help shape creatively. My favourite thing to do is stand at a whiteboard with my colleagues to discuss ideas and develop solutions together.

What tasks do you deal with on a daily basis?

That’s not an easy question to answer, as my job deliberately addresses several aspects. At the moment, a lot of my time revolves around the topic of “making school transport efficient” – in other words, optimising school transport. We’ve developed a tool for this, and I can support and shape this process as a product owner. This means that I have at least one foot in all projects on this topic, so I’m always on top of things. I ask myself the following questions: What else does this tool need? What else do we need to integrate, develop and consider in order to make it even better and more versatile? First and foremost, this means a lot of communication with the various stakeholders involved in order to clarify requirements, research possible solutions and plan and prioritise further development accordingly.

What are mobility analyses for school transport and why are they so important?

School transport accounts for a large proportion of all bus transport, especially in rural areas, and ties up a particularly large number of drivers and vehicles due to peak times at the beginning and end of the school day. This makes it the main cost driver. When it comes to issues of cost efficiency and overcoming the shortage of drivers in public transport, it is therefore essential to consider school transport. The special thing about transport services for schoolchildren is that there are often legal regulations that must be complied with. It starts with a clear definition of who must receive at least one mobility offer and goes on to detailed specifications such as: How long can the journey to school take? How many transfers may be included? How long can pupils arrive early before school and what time do they have to be there at the latest?

So, we have a system that is enormously complex, generates high costs and is based on timetables that have evolved over time. A demand-oriented, data-based and algorithm-supported approach therefore harbours great potential for increasing efficiency and cutting costs.

What is special about the methodology for analysing school transport?

What is special about our approach to analysing and redesigning school transport is that it is user-based and uses modern algorithms. To do this, we create a microscopic model from actual school data, which allows us to obtain a detailed picture of the existing school transport system and defines the needs that the new network must cover. Using modern algorithms, we generate scheduled journeys that provide all pupils in the model with a service within certain quality standards, such as maximum journey time, while requiring as few round trips as possible. By focussing on a small number of vehicles, we can create a more efficient system with fewer pronounced peak loads. In addition, we can also evaluate school time staggering in terms of its effect on vehicle utilisation, costs and quality. 

What was the journey from the initial idea to the finalised solution?

The topic of school transport came to us when we realised that school bus transport was a major pain point for many local authorities and transport companies. At the same time, there was no suitable solution on the market. Since innovation and the development of demand-orientated and algorithm-supported methods are in our DNA, we have taken on this topic. After researching and trying out various approaches, we finally wrote the first proof of concept. Since then, we have continued to develop the approach to keep pace with the requirements of new projects and to constantly improve. 

Thanks Milena!

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