Harnessing Large Language Models for Efficient Crowd Management in Large-Scale Events

Managing large public events involves significant challenges, including transportation congestion, attendee coordination, and enhancing the overall event experience. This paper introduces a novel approach that integrates Large Language Models (LLMs) and generative AI to address these issues effectively. We present a framework that leverages enriched knowledge graphs comprising diverse datasets, such as geographic information, transportation systems, environmental factors, and more, to recommend personalised, context-aware itineraries. By employing LLMs, we aim to manage attendee flow, stagger departure times, and guide individuals through engaging points of interest. Additionally, we incorporate generative AI to design gamified content, such as interactive quizzes and puzzles, tailored to user preferences. These gamification elements not only provide entertainment but also encourage staggered event departures, mitigating post-event congestion. The experimental study conducted in Milan demonstrated the effectiveness of the proposed system: AI-generated itineraries closely matched expected travel times, with minimal deviations of 2–5 minutes. Moreover, responses to the user experience questionnaire reflected high levels of usability, engagement, and overall satisfaction, reinforcing the potential of this approach for improving post-event mobility and attendee experience.