Agent-based vulnerability assessment at airport security checkpoints

Agent-based vulnerability assessment at airport security checkpoints: A case study on security operator behavior

Janssen, S.A.M. (TU Delft Air Transport & Operations)
van den Berg, Arjan (Student TU Delft)
Sharpanskykh, Alexei (TU Delft Air Transport & Operations)

2020

Despite enormous investments in airport security, terrorists have been able to find and exploit vulnerabilities at security checkpoints. Existing vulnerability assessment methodologies struggle with accounting for human behavior, and agent-based modelling forms a promising technique to overcome this limitation. This paper investigated how the decision-making and performance of human operators can be taken into account while assessing vulnerability at an airport security checkpoint. To this end, an agent-based model was designed, in which the performance of security operators was modelled using a functional state model, while decision making was modelled using decision field theory. Passengers and an attacker that brings a weapon to the security checkpoint were also explicitly modelled as agents. Simulation results indicate that the highest skilled operators outperformed their lowest skilled counterparts on analyzing X-ray images, but performed worse on both searching luggage and performing patdowns. Furthermore, results showed that a high focus on speed of security operators leads to a decrease in luggage searches and therefore increased vulnerability. More work is needed to calibrate and validate the simulation results, but initial results are promising. The agent-based model can be used by airport regulators and managers to understand the workings of their security checkpoint better and ultimately to reduce vulnerabilities.

Agent-based modelling
Human decision making
Human performance
Security
Vulnerability

http://resolver.tudelft.nl/uuid:13bb1ddc-a508-4790-b460-d1ae12191d72

https://doi.org/10.1016/j.trip.2020.100139

Transportation Research Interdisciplinary Perspectives, 5

Institutional Repository

journal article

© 2020 S.A.M. Janssen, Arjan van den Berg, Alexei Sharpanskykh