Researchers at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB are developing an artificial intelligence as part of the DAKIMO project to facilitate intelligent, intermodal transportation systems in Germany. This AI is designed to integrate public transport, ride-sharing, and micro-mobility services into optimised travel routes, aiming to provide seamless and reliable journeys. The initiative seeks to encourage a shift away from reliance on privately owned vehicles, addressing concerns regarding carbon emissions from the dominant mode of transport in Germany.
The Challenge of Sustainable Mobility
Despite growing environmental awareness, cars remain the dominant transportation choice in Germany and across much of the world. The reason is straightforward: cars offer unmatched convenience. They’re always available, require minimal planning, and provide door-to-door service. Meanwhile, sustainable alternatives like buses, trains, shared bikes, and electric scooters often involve complex coordination between different services.
The environmental case for change is compelling. Public transportation produces significantly fewer carbon emissions per passenger than private vehicles. Yet the complexity of planning intermodal journeys—trips that combine multiple transportation modes—keeps many people reaching for their car keys instead of exploring greener alternatives.
An AI Solution for Complex Planning
The DAKIMO project, led by researchers at Fraunhofer IOSB in Karlsruhe, addresses this planning challenge with artificial intelligence. Their system predicts the availability of shared transportation options, incorporating real-time traffic data and historical usage patterns to forecast where bikes and scooters will be available at specific times and locations.
“For transportation to be intermodal and thus more ecofriendly, it needs to be simpler, more reliable, more flexible, and easier to plan for,” explains Jens Ziehn, the project’s lead researcher. The AI steps in precisely where human planning becomes overwhelming—when buses run late, bikes disappear from stations, or multiple transportation options create too many variables to consider manually.
The technology divides geographical areas into small cells and uses short time intervals to calculate both immediate and future availability probabilities. By analyzing open data sources including public transit information and historical sharing vehicle locations, the system can predict transportation options with remarkable accuracy.
From Research to Real-World Application
The practical impact of this research extends beyond academic study. Project partner raumobil GmbH has integrated these AI forecasts into route planning algorithms, creating a mobility app that can recommend optimal transportation combinations from start to destination. The technology is being tested through Karlsruhe’s regiomove app, which serves the Middle Upper Rhine Region.
Perhaps most significantly, the forecasting capabilities are being incorporated into the General Bikeshare Feed Specification (GBFS), an international standard for sharing transportation data. This integration means the technology could eventually power routing apps worldwide, transforming how millions of people plan their daily travels.
Promising Early Results
Public response to the AI-enhanced transportation planning has been overwhelmingly positive. A comprehensive study involving over 1,500 participants found that nearly 90 percent view AI-based predictions for shared transportation as helpful or very helpful. More importantly for environmental goals, approximately 20 percent of survey respondents indicated they would occasionally leave their cars at home in favor of public transit when equipped with better planning tools.
The project, funded with 3.5 million euros by the German Federal Ministry of Research, Technology and Space, represents a collaborative effort involving multiple research institutions and transportation companies. Plans call for expanding the forecasting model throughout Baden-Württemberg state, with potential for much broader implementation.
The Road Ahead
As cities worldwide grapple with climate change and urban congestion, the DAKIMO project offers a glimpse of how artificial intelligence could accelerate the transition to sustainable transportation. By making car-free travel as convenient as driving, this technology addresses one of the fundamental barriers to reducing transportation emissions.
The research confirms that when given reliable, intelligent planning tools, people are willing to change their transportation habits. The question now is how quickly this innovation can scale to transform mobility systems globally.
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