This paper presents novel technology and methodology aimed at enhancing crowd management in both the planning and operational phases. The approach encompasses innovative data collection techniques, data integration, and visualization using a 3D Digital Twin, along with the incorporation of artificial intelligence (AI) tools for risk identification. The paper introduces the Bowtie model, a comprehensive framework designed to assess and predict risk levels. The model combines objective estimations and predictions, such as traffic flow operations and crowdedness levels, with various aggravating factors like weather conditions, sentiments, and the purpose of visitors, to evaluate the expected risk of incidents. The proposed framework is applied to the Crowd Safety Manager project in Scheveningen, where the DigiTwin is developed based on a wealth of real-time data sources. One noteworthy data source is Resono, offering insights into the number of visitors and their movements, leveraging a mobile phone panel of over 2 million users in the Netherlands. Particular attention is given to the left-hand side of the Bowtie, which includes state estimation, prediction, and forecasting. Notably, the focus is on generating multi-day ahead forecasts for event-planning purposes using Resono data. Advanced machine learning techniques, including the XGBoost framework, are compared, with XGBoost demonstrating the most accurate forecasts. The results indicate that the predictions are adequately accurate. However, certain locations may benefit from additional input data to further enhance prediction quality. Despite these limitations, this work contributes to a more effective crowd management system and opens avenues for further advancements in this critical field.

翻译：本文提出了旨在增强人群管理在规划与运营阶段的新技术与方法论。该方法涵盖创新数据采集技术、数据集成及基于三维数字孪生的可视化，并结合人工智能工具进行风险识别。本文介绍了Bowtie模型这一综合性框架，用于评估和预测风险水平。该模型将客观估算与预测（如交通流运行状态与人群密集度）与各类加剧因素（如天气状况、情绪及访客目的）相结合，以评估事件发生的预期风险。所提出的框架已应用于斯海弗宁恩的“人群安全管理者”项目，数字孪生系统基于海量实时数据源开发。值得关注的数据源之一为Resono，该平台依托荷兰超过200万用户的手机面板数据，提供访客数量及其移动轨迹洞察。本研究重点关注Bowtie模型的左侧部分，涵盖状态估计、预测与预报。特别地，研究聚焦于利用Resono数据生成多日前预报以支持活动规划。通过对比包括XGBoost框架在内的先进机器学习技术，XGBoost展现了最优的预测精度。结果表明预测具有足够准确性，但部分地点可能需要引入额外输入数据以进一步提升预测质量。尽管存在这些局限，本工作仍为构建更有效的人群管理系统做出了贡献，并为这一关键领域的进一步发展开辟了道路。