Explainable AI for Dispositive Measures in Public Transport
From disruption to action: explainable AI shows which decision helps in public transport - and why.
Context
This large-scale research project was delivered by our founders through LAVRIO.solutions GmbH as part of Germany's KARL Competence Center.
The Challenge
Public transit control centers face high-pressure decisions in real-time: delays cascade, resources must be reallocated, and passengers need accurate information. Operators must process vast amounts of data while making split-second decisions that affect thousands of commuters.
The Solution
Explainable AI systems that support control room operators in local public transport. The AI analyzes complex situations, suggests optimal responses, and—critically—explains its reasoning so operators can make informed decisions rather than blindly following recommendations.
Our Founders' Role
Through LAVRIO.solutions, our founders contributed the explainable AI methodology, ensuring that algorithmic recommendations come with clear justifications. They developed visualization techniques that make AI reasoning accessible to operators under time pressure.
Project Scope
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Large-scale research initiative (€8 million funding)
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Consortium of leading German research institutions and industry partners
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Focus on human-AI collaboration in time-critical environments
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Published research on XAI for decision support in control centers
Published Paper
Funded by
German Federal Ministry of Education and Research (BMBF)