用数字孪生技术优化地铁站安防布局，提升可疑行为识别率。
Public space security management using digital twin technologies
- 构建雅典地铁站数字孪生模型，模拟客流与重点区域。
- 调整摄像头位置和角度后，可疑行为检测率显著提升。
- 适合智慧安防、城市治理研究者参考。
当前公共空间安全问题日益突出，数字孪生技术为威胁检测与预测提供了新方案。本研究以希腊雅典一地铁站为对象，基于FlexSim仿真软件构建数字孪生模型，涵盖关键区域与乘客流动，并设定相应参数。该模型可针对不同场景进行安全态势预测。通过实验测试多种监控摄像头配置及角度优化，验证了安防部署效果。结果表明，合理规划摄像头位置与视角能显著提升可疑行为的识别能力。结合数字孪生技术，可对多种场景进行评估并找到最优监控配置。研究表明，数字孪生在实时仿真与数据驱动的安全管理中具有重要价值，为公共空间智能安防提供创新框架。
原文摘要 · Abstract (English)
As the security of public spaces remains a critical issue in today's world, Digital Twin technologies have emerged in recent years as a promising solution for detecting and predicting potential future threats. The applied methodology leverages a Digital Twin of a metro station in Athens, Greece, using the FlexSim simulation software. The model encompasses points of interest and passenger flows, and sets their corresponding parameters. These elements influence and allow the model to provide reasonable predictions on the security management of the station under various scenarios. Experimental tests are conducted with different configurations of surveillance cameras and optimizations of camera angles to evaluate the effectiveness of the space surveillance setup. The results show that the strategic positioning of surveillance cameras and the adjustment of their angles significantly improves the detection of suspicious behaviors and with the use of the DT it is possible to evaluate different scenarios and find the optimal camera setup for each case. In summary, this study highlights the value of Digital Twins in real-time simulation and data-driven security management. The proposed approach contributes to the ongoing development of smart security solutions for public spaces and provides an innovative framework for threat detection and prevention.
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