Dynamic Bayesian Network for Predicting the Likelihood of a Terrorist Attack at Critical Transportation Infrastructure Facilities
Publication: Journal of Infrastructure Systems
Volume 15, Issue 1
Abstract
This research is motivated by the increased awareness for terrorism related research in the post- era. Transportation infrastructures such as airports, metro and subway systems, bridges, and tunnels are vital to the U.S. economy. Therefore, in the wake of recent terrorist incidents, innovative and robust methods need to be exploited to predict the likelihood of terrorist strikes at critical transportation infrastructure facilities. Since the greater need for predicting future terrorist activities has only been recognized in recent years, the research in this field is in the nascent stages. There are two key aspects to predicting future terrorist activities: (1) developing a reliable and robust prediction model; and (2) analyzing the precision and reliability of available intelligence and other relevant information needed to develop a prediction model. This paper focuses on the first aspect and develops a terrorist attack prediction model (TAPM) using dynamic Bayesian networks (DBNs), which can be used to predict the likelihood of future terrorist activities at critical transportation infrastructure facilities. Theoretical development of the TAPM is presented and the model is employed in two examples to predict a terrorist strike with the possibility of an airplane hijack at a typical U.S. airport. The results suggest that the proposed DBN approach, although more data intensive, may provide a more reliable and better prediction. Since the nature of terrorist strikes is different than natural strikes, a game theoretic approach may be suggested in future works. Moreover, considering uncertainty in data as well as possibility theory in lieu of probability theory are also suggested for future research. This research is the significant first step in terrorist activity prediction at critical infrastructure facilities. Future enhancements to the model for more reliable predictions using real-life attributes are discussed.
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Acknowledgments
This work was motivated by the writer’s summer 2005 work as a research fellow at the START (Study of Terrorism and Responses to Terrorism) Center of Excellence at the University of Maryland, College Park funded by the Department of Homeland Security. The writer thanks his student researcher Kimberly Freeman for her assistance with the literature review while working in the research team at the START center. This work, in part, was completed at the Center for Advanced Transportation and Infrastructure Engineering Research (CATIER) (www.eng.morgan.edu/∼catier) at Morgan State University. Currently, the writer is working on characterizing the influence of geography and terrain in modeling insurgent activities in Iraq. That work is part of Morgan State University’s (MSU’s) Knowledge Integration and Management Center of Excellence (KIMCOE) funded by the Army Research Lab (ARL). Thanks are due to the Dean of the School of Engineering, Dr. Eugene Deloatch who is the Principal Investigator (PI) for the KIMCOE research project. The writer also thanks his KIMCOE research collaborators Dr. LeeRoy Bronner and Dr. Bheem Kattel, and ARL project manager Dr. Dana Ulery. Finally, the anonymous reviewers are thanked for their many valuable comments, which enhanced the quality of the paper.
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 15 • Issue 1 • March 2009
Pages: 31 - 39
Copyright
© 2009 ASCE.
History
Received: Mar 3, 2006
Accepted: Jun 15, 2007
Published online: Mar 1, 2009
Published in print: Mar 2009
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