Fraunhofer IOSB presents: Mobility Transition with DAKIMO

From the perspective of SpectroNet, the work of our member Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB is a strong example of how intelligent data analysis and AI can accelerate the transition toward sustainable mobility. Under the guiding theme “Sustainable Mobility: Getting There Faster With AI”, researchers in Karlsruhe are addressing one of the key challenges of climate-friendly transportation: making eco-friendly alternatives as convenient and reliable as private cars.

Despite their high carbon emissions, cars remain the number one mode of transportation in Germany. Public transport, shared bikes, and electric scooters offer far lower environmental impact, yet many people still rely on private vehicles because they are always available and easy to use. Fraunhofer IOSB is tackling exactly this issue within the DAKIMO project, which focuses on intelligent, intermodal transportation without the need for privately owned cars. The goal is to enable seamless, convenient, and reliable journeys by intelligently combining different modes of transport.

A major barrier to intermodal mobility today is complexity. Travelers may be able to reach a transfer point quickly by bus or tram, but uncertainty about the availability of shared bikes or e-scooters often discourages them from using such options. Existing routing apps rarely consider these uncertainties. This is where Fraunhofer IOSB’s AI-based approach makes a decisive difference. The researchers have developed a system that predicts the availability of shared means of transportation, taking into account live traffic data, historical usage patterns, and spatial-temporal factors. The AI calculates probabilities for finding a bike or scooter at a specific location and time, enabling much more reliable route planning.

In cooperation with project partner raumobil GmbH, these forecasts are integrated into intermodal routing within a mobility app. Instead of simply showing theoretical connections, the app recommends routes that factor in predicted availability, travel times, and transfer points. The long-term vision is to enhance the regiomove app operated by Karlsruher Verkehrsverbund (KVV), allowing users to receive customized, situation-aware route suggestions that fit their individual needs just as effortlessly as grabbing car keys.

As Jens Ziehn, project lead at Fraunhofer IOSB, emphasizes, intermodal transportation must be simpler, more flexible, and easier to plan in order to succeed. The AI supports users precisely where human planning reaches its limits — for example, when a bus is delayed or when shared bikes unexpectedly run out at a transfer point. By dividing urban areas into small geographic cells and short time intervals, the AI can generate short- and long-term forecasts of vehicle availability based on open data sources and historical information.

Beyond the app itself, Fraunhofer IOSB is contributing to systemic change by helping to expand the General Bikeshare Feed Specification (GBFS), an international standard for real-time transportation data. The aim is to integrate AI-based forecast probabilities into the standard, enabling routing apps worldwide to go beyond displaying current vehicle locations and instead offer reliable predictions of future availability. This expansion has already been accepted by MobilityData, and a one-year evaluation phase is currently underway. The AI fusion server that aggregates and processes the data is already operational, and the forecasting feature is part of a test version of the regiomove app, with plans to roll it out across Baden-Württemberg.

The public response underscores the relevance of this work. In a study involving more than 1,500 participants, nearly 90 percent rated AI-based availability predictions for shared mobility as helpful or very helpful, and around 20 percent stated they would occasionally leave their cars at home in favor of public transport. For SpectroNet, these results clearly demonstrate how AI, combined with intelligent data standards and system integration, can actively support the mobility transition and contribute to climate action.

With DAKIMO, Fraunhofer IOSB shows how applied research can translate cutting-edge technologies into practical solutions that make sustainable mobility not only possible, but genuinely attractive.

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