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Developing AI Solutions Through Seven Real-World Use Cases

AiMTT is developing AI-driven solutions through seven real-world use cases, each designed to create practical tools that can be directly implemented.

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 like SAIL 2025.

2. Network-Wide Traffic Management

AI will be used to monitor, predict, and optimize traffic flows on a larger scale, enhancing overall mobility and reducing congestion.

3. Optimized Infrastructure Maintenance

AI-powered tools will support efficient infrastructure maintenance planning, enabling timely repairs while minimizing disruptions.

4. AI in Urban Construction Logistics

By optimizing the logistics of urban construction projects, this use case aims to reduce disruptions to city mobility and improve project efficiency through AI.

5. 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.

6. Enhancing Public and Target Group Transportation

AI solutions will be applied to optimize public transportation and specialized services in various regions of the Netherlands.

7. Smarter Inland Shipping and Container Transport

AI-driven tools will be developed to improve the efficiency, speed, and flexibility of planning for inland shipping and container transport.