Use Case #3: Visitor and Crowd 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.

Background

Managing large numbers of visitors during busy periods—such as shopping weekends, peak beach days, football matches, and major events—requires fast and well-informed decisions. While AI can provide valuable estimates and predictions of visitor flows, professionals often find it difficult to assess how reliable these predictions are or how they should be used in practice. This use case focuses on helping visitor management professionals understand, interpret and confidently apply AI-supported insights in operational decision-making.

Objective

The project aims to strengthen AI literacy among visitor and crowd management professionals by improving their ability to interpret, evaluate and use AI-supported estimates and predictions. It investigates how explainability, transparency and uncertainty information influence professionals’ trust in AI and the quality of operational decision-making.

Approach

The partners apply existing AI methods—including AI-supported prediction models, explainable AI and digital twin technology—in real-world visitor management scenarios. Through learning-by-doing sessions, professionals explore how to interpret AI predictions, assess uncertainty and combine AI insights with their own expertise. The knowledge gained is translated into transferable learning modules, educational materials and practical guidance that can be applied across the AiMTT Learning Community and in other mobility domains.