2021 – Present
This project addresses the lack of effective digital twin technology for identifying and mitigating security risks at soft targets and crowded places. The challenges to making digital twins, which are virtual representations of physical assets, environments or systems that can be used to simulate their behavior to better understand how they work, a practical solution for enhancing security include data integration issues, limited real-time monitoring, lack of human digital twin teaming, and high costs. The primary objective is to design, experiment with, and evaluate scalable and effective methods for creating dynamic digital twins of soft targets and crowded places that enhance security by monitoring environments in real-time, predicting threats, and integrating crowd behavior models to mitigate vulnerabilities.
The project integrates several methodologies and technologies, including commodity building IoT solutions, AI methods, real-time data assimilation, and human-technology-environment interaction modeling. This integrated approach leads to tools that can simulate and monitor environments, predict security concerns, and optimize responses to threats. The project aims to implement its findings by developing decision simulators for stakeholders, including facility operators and public safety officials. These simulators, based on the project’s dynamic digital twin technology, will enable users to quickly explore alternative emergency scenarios and make real-time decisions. The tools will enable personnel training prior to events, support facility control during crises, and improve security practices for soft targets and crowded venues.
The project improves the state of the art by advancing scalable facility modeling methods, integrating subsystem digital twins, combining physical observations with virtual models, and employing human-digital twin teaming for decision support during extreme events. It also enhances verification and validation techniques for using digital twins in security contexts.
In 2024, the project developed a decision simulator for stakeholders at Rutgers University’s Football Stadium, enabling real-time emergency simulations and data assimilation for decision-making. It also completed modeling cyber-physical systems in transit terminals, with real-time monitoring of crowd flow and operations.
This project collaborates with projects in the Layered Security Architectural Design and Simulation Research Area, as well as industry partners such as transportation facility operators and public safety officials. The project is connected to SENTRY’s Passenger-based Transportation Case Study involving the NJ Transit Hoboken Terminal, and NJ Transit Secaucus Junction Station, and SENTRY’s Stadium Security Case Study, which engages Rutgers Football Stadium. These partnerships facilitate real-world testing and integration of digital twin technologies by providing access to data, testing environments, and practical feedback. Collaborations help ensure that the digital twin technology is both applicable and scalable for use in protecting soft targets and crowded places in real-world scenarios.
