Communications of the ACM
Mathematical Model Effectively Screens Airline Passengers
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Passenger screenings at the nation's airports can be conducted more efficiently without compromising aviation security, according to research at Virginia Commonwealth University and the University of Illinois at Urbana-Champaign.
Changes in aviation security policies and operations since the Sept. 11, 2001, terrorist attacks have resulted in every passenger being treated as a potential security risk, with uniform screening of passengers and their luggage. Screening all passengers the same way is costly and inconvenient for air travelers, according to the research, published in the June 2009 issue of IIE Transactions.
"We set out to find a real-time screening methodology that considers both available screening resources and the necessity of being robust in assessing threat levels," said Laura A. McLay, Ph.D., an assistant professor in the VCU Department of Statistical Sciences & Operations Research. "This paper provides methodology to quickly determine which passengers are high-risk and who is low-risk and screen them accordingly," McLay said.
McLay co-authored the report with Sheldon H. Jacobson, Ph.D., a professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign, and Alexander G. Nikolaev, Ph.D., a visiting scholar in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign.
The researchers considered a risk-based model in which passengers are classified as selectees (high-risk) or non-selectees (low-risk). Screening procedures are in place for each classification and selectees would undergo additional screening.
The challenge is that passengers arrive at the airport one at a time and how risky each passenger is becomes known only when they check in for their flight. The model's objective is to use these passenger risk levels to determine the best policy for screening passengers to detect threats in the system given there are limited screening resources.
"If you only can label 100 passengers as high-risk due to screening capacity limitations, then ideally you'd like to pick the 100 passengers with the highest risk scores," McLay said. "But since you can't look into the future and know exactly who is going to arrive, you have to look to make some difficult choices in real-time. Our model provides a methodology for translating the risk scores to a screening decision."
The research was funded by the National Science Foundation, the Air Force Office of Scientific Research and the Department of Homeland Security.
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