“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.
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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.
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.
LATEST NEWS
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TU Delft Students Develop AI Solutions for AiMTT Crowd Management Case
TU Delft has incorporated elements of the AiMTT use case Crowd Management During Events into two courses of the Master’s programme in Transport, Infrastructure and Logistics. More than thirty students worked on designing (course TIL4030) and building (TIL6022) smart AI solutions for crowd management. TIL4030 The aim of the course TIL4030, Research and Design Methods,
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Results of Crowd Management Use Case Integrated into Master’s Programme
During the most recent edition of SAIL Amsterdam, significant progress was made on the AiMTT use case Crowd Management During Events. Among other achievements, the partners developed an AI model capable of predicting pedestrian crowd levels several hours in advance. Parts of the use case have now been incorporated into the Master’s programme in Transport,