# Public space security management using digital twin technologies

arXiv: https://arxiv.org/abs/2503.06996 · HTML: https://mlsanity.com/paper/2503.06996/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Self-supervised visual learning in the low-data regime: a comparative evaluation](https://mlsanity.com/paper/2404.17202/) (2024)
- [A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence](https://mlsanity.com/paper/2311.01154/) (2023)
- [User profile-driven large-scale multi-agent learning from demonstration in federated human-robot collaborative environments](https://mlsanity.com/paper/2103.16434/) (2021)
- [An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE](https://mlsanity.com/paper/2104.07234/) (2021)
- [Leveraging Digital Twin Technologies for Public Space Protection and Vulnerability Assessment](https://mlsanity.com/paper/2408.17136/) (2024)

## Abstract (arXiv)

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.