The 9/11 terrorist activity followed by Madrid and London mass transit bombings have had far-reaching effects on transportation infrastructure security planning. Transportation infrastructures (such as, airports, metro and subway stations, bridges, and tunnels) are vital to any nation's economy; therefore, in the wake of recent terrorist incidents innovative and robust methods need to be exploited to predict likelihood of terrorist strikes. This research is motivated by the growing vulnerability of transportation infrastructures in the post 9/11 era. Bayesian Networks (BNs) have been proven to be very effective in predicting future scenarios, such as weather forecasts and market predictions. In this paper a BN model is developed for predicting likelihood of a terrorist strike at a critical transportation infrastructure facility, such as an airport or a mass transit station that acts as a hub. An example study is performed to demonstrate the effectiveness of the developed approach. Limitations of the proposed approach are outlined and Dynamic Bayesian Networks are suggested to be exploited in the future for solving real-world problems at the expense of lower computational burden.


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    Title :

    Applying Bayesian Networks to Assess Vulnerability of Critical Transportation Infrastructure


    Contributors:
    Jha, M. K. (author)

    Conference:

    Ninth International Conference on Applications of Advanced Technology in Transportation (AATT) ; 2006 ; Chicago, Illinois, United States



    Publication date :

    2006-08-04




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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