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Learning and Developing Through Real-World Use Cases
AiMTT’s partners are learning and developing together through six use cases based on real-world challenges. In each use case we work towards practical AI solutions designed for implementation.
1. Crowd Management During Events
This use case explores how AI models can monitor crowd movements, predict bottlenecks and risks, and recommend measures to improve safety and flow at large events such as SAIL 2025. Read more…
2. Decentralized Traffic Management
Through AI, we explore how traffic management can achieve central network goals while enabling decentralized decision-making. Read more…
3. Visitor Management in Busy Areas
This use case explores how AI can support data-driven visitor and crowd management in coastal and inner-city areas, focusing on explainable predictions of visitor flows and crowd levels.
4. Assessing the Effects of Traffic Management and Transportation Policies
Using AI, we investigate how to develop a more robust understanding of the effects of traffic management and transportation policies.
5. Smarter Inland Shipping and Container Transport
We will develop AI-driven tools to improve the efficiency, speed, and flexibility of inland shipping and container transport planning.
6. AI in New In-Car Technologies
This use case supports vehicle inspectors in assessing the benefits and risks of emerging AI-powered in-car technologies.