Leveraging Large Language Models for Predictive Human Mobility Analytics in Mass Gathering Environments

The rapid growth of mass gathering events — spanning sports tournaments, religious congregations, political rallies, and cultural festivals — presents significant challenges for public safety authorities, urban planners, and emergency response teams. Traditional crowd management approaches, largely reliant on historical data and manual observation, often fall short in providing real-time, adaptive, and accurate predictions of human mobility patterns. This study proposes a novel data-driven framework that leverages Large Language Models (LLMs) to enhance predictive human mobility analytics in mass gathering environments, with the overarching goal of supporting informed decision-making and proactive safety planning. The proposed framework integrates multi-source heterogeneous data — including social media streams, geospatial records, event schedules, transportation logs, and historical crowd movement datasets — to train and fine-tune LLM-based models capable of capturing complex spatiotemporal mobility patterns. By exploiting the contextual reasoning and natural language understanding capabilities of LLMs, the system interprets unstructured situational data and translates it into actionable crowd movement forecasts. The model further incorporates real-time event dynamics, environmental variables, and behavioral indicators to continuously refine its predictive outputs.Experimental evaluations conducted across multiple large-scale public event datasets demonstrate that the proposed LLM-based approach significantly outperforms conventional machine learning baselines in prediction accuracy, adaptability, and interpretability. The framework also supports scenario simulation and risk zoning, enabling safety personnel to anticipate crowd surges, identify high-density hotspots, and optimize resource deployment well in advance. Furthermore, the integration of an explainable AI component ensures that decision-makers receive transparent, human-readable justifications for model predictions, fostering trust and operational confidence