“Learning by Doing” to Develop AI Applications for Mobility
AiMTT aims to cultivate a highly skilled and diverse AI talent pool equipped to address the opportunities and challenges of AI in mobility, transport, and logistics. By combining real-world case studies with knowledge development, this initiative fosters deep expertise in the field.
Our project partners will build, test, and refine AI applications for mobility, transport, and logistics through seven real-world use cases. These tools will be ready for practical implementation. Equally important, however, is the learning process that comes with working hands-on with AI. To support this, AiMTT offers workshops, training programs, and co-creation sessions—ensuring continuous knowledge exchange and improvement.
Seven Use Cases
AiMTT is developing AI-driven solutions through seven real-world use cases, each designed to deliver practical mobility tools ready for direct implementation. The use cases range from crowd management at large events to smarter, more efficient solutions for inland shipping.
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More Than Twenty Partners
The AiMTT consortium brings together over twenty partners, carefully selected for their ability to address key mobility, transport, and logistics challenges, maximize impact, and create meaningful learning opportunities for stakeholders. This collaboration also offers strong potential for project-based training and education.
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4-Day Course: Data, AI and Multimodal Traffic Management
This four-day course brings together PhD researchers and mobility professionals to explore how artificial intelligence and data can be used to manage complex, multimodal traffic systems.
LATEST NEWS
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First AiMTT Tutorial ‘AI in Mobility’ Published in NM Magazine
The journal NM Magazine has published the first tutorial in the AiMTT series ‘AI in Mobility.’ The Dutch-language magazine, widely distributed among traffic professionals in the Netherlands and Flanders, reserved three pages for the article ‘Large Language Models – Shaping the Future of Transportation?’ This was a translated and slightly shortened version of the AiMTT…