05 September 2023
Data and artificial intelligence for more flexible, sustainable mobility
Launched in 2021, the DAKIMO research project aims to make sustainable, "intermodal" mobility more attractive and seamless using artificial intelligence (AI) methods.
The DAKIMO project (data and AI as enablers for sustainable, intermodal mobility, dakimo.server.de) is specifically aimed at the challenge of multi- and intermodal mobility, i.e. the flexible change between different modes of transport - not only depending on the destination, but also depending on the leg of the journey. The project is funded by the Federal Ministry of Education and Research (BMBF), supported by the Karlsruhe Project Management Organisation and brings together the following Karlsruhe institutions:
- Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
- raumobil GmbH
- INIT Innovative Informatikanwendungen in Transport-, Verkehrs- und Leitsystemen GmbH
- INOVAPLAN GmbH
- Institut für Verkehrswesen am Karlsruher Institut für Technologie (KIT)
- KVV – Karlsruher Verkehrsverbund
The partners are united by the goal of offering mobility services that get people to their destinations reliably, conveniently, flexibly and sustainably using a wide range of data from end users, local authorities and transport companies - and with their intelligent fusion.
The mobility transformation as an opportunity and a challenge
The transformation of mobility offers considerable potential for combining economic efficiency, sustainability, environmental friendliness and individuality. However, in order to utilise this potential, the complexity of offers and social needs must first become manageable. "The motivations for mobility and the choice of means of transport are complex," points out PD Dr Martin Kagerbauer from KIT. If society is to use intermodal, more sustainable mobility solutions, the use of an intermodal connection chain - such as the combination of (hire) bike, public transport and e-scooter - must be at least as easy and convenient as picking up your own car keys.
The project goal: making intermodal mobility manageable with AI
One aim of the DAKIMO project is therefore to expand the regiomove app of the Karlsruhe transport association KVV. The partners are investigating how the potential of data already available from apps, public transport operations and traffic and weather forecasts, for example, can be exploited to provide users with tailored suggestions for means of transport that best suit their needs and route, improve services and remove barriers to using sustainable means of transport. Kagerbauer: "We at KIT are contributing our expertise in mobility behavior and modeling to DAKIMO. There is an interdisciplinary collaboration in the project that is very fruitful." His colleague Pia Tulodetzki adds: "In the project, we are analyzing the influence of the weather on mobility behavior using surveys and other data sources. We are seeing fascinating correlations here."
Fraunhofer IOSB is responsible for the overall project management of DAKIMO. Project manager Jens Ziehn says: "In the project, we see directly how mobility can develop further with data. We are using simulations to help train AI processes to evaluate data in a data protection-safe manner. The aim is to enable citizens to benefit from AI without taking avoidable risks."
Opportunities for local authorities and companies
DAKIMO also offers opportunities in the harmonisation of ongoing activities in the areas of mobility data spaces, networking and automation, as well as individual mobility and information services. An AI-supported data ecosystem is intended to promote the mobility transition: Manufacturers, operators and service providers should understand potentials faster and more precisely, and thus design sustainably successful offers. Accordingly, the project results will be incorporated into the development of the KI-Allianz Baden-Württemberg eG data platform.
"In DAKIMO, we have set ourselves the goal of further developing our mobility platforms by offering new data-driven services. This includes improved and, of course, data protection-compliant collection and analysis of app usage data and the integration of new, AI-supported mobility information," says Tanja Klopper, Product Manager for Analysis and Project Manager for DAKIMO at partner raumobil, which is developing the regiomove app, among other things.
Municipalities and transport companies can build on this to provide better and more appropriate mobility services. Transport demand models are also incorporated into the results of the data analyses and AI. The agent-based transport demand model mobiTopp is used here. The traffic demand model makes it possible to model various future scenarios and quantify their effects. Gabriel Wilkes from KIT explains: "With our agent-based modelling framework mobiTopp, we can accurately simulate mobility. Taking the weather aspect into account makes the simulations even more realistic."
A step towards sustainable, accessible mobility
The first prototype version of the AI fusion server, which can be used to make AI-based route suggestions, is due to go live in autumn 2023. After the end of the project, the technical developments are to be incorporated into the KVV.regiomove app from 2025. They will also support scheduling in public transport operations and transport planning in the municipalities.
"I hope that with DAKIMO we can contribute to better services for local authorities, mobility providers and users, for example better journey planning through improved transport models. After all, mobility concerns us all and is an important part of our everyday lives," says Tim Hilgert, who is leading the project at partner INOVAPLAN, summarising the objective. The project alone will therefore not solve the mobility transition, but it should make a tangible contribution to making sustainable mobility more accessible and convenient.
Note: This research and development project is funded by the German Federal Ministry of Education and Research (BMBF) as part of the "Future of Value Creation - Research on Production, Services and Work" programme and supervised by the Project Management Agency Karlsruhe (PTKA). The responsibility for the content of this publication lies with the authors.
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