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Improving the total performance of th e air traffic management (ATM) system in terms of capacity, safety, efficiency, and flexibility require dramatic system -wide transformations as well as in the required performance levels for individual communication, navigation, and surveillance (CNS) syst ems . The evaluation of specific performance levels of ATM system requires a structural robust modeling and simulation framework that can evaluate emergent system -wide performance arising from the behavior of individual system components including human ope rators. This paper proposes an agent -based modeling and simulation framework including computational human performance models, built upon cognitive engineering principles, for a priori computational modeling and analysis method of predicting the required p erformance of potential grouping of innovative CNS technologies and applications proposed for Next Generation Air Transportation System (NEXTGEN) operations. A specific test case of analyzing aircraft arrivals into LAX using Continuous Descent Arrival (CDA ) concept will be examined as a demonstration. I. Introduction IR traffic demand has been steadily increasing for the past several decades, leading to airport and airspace congestion, with corresponding flight delays. Improving the performance of the air tr affic management (ATM) system in terms of capacity, safety, efficiency, and flexibility requires innovative transformations. These transformations will require dramatic changes in the required performance levels for technologies and applications, and for c ommunication, navigation, and surveillance (CNS) systems to cope with the growing demand of air traffic and to provide safe, consistent air traffic services (ATS). 1 The overall performance of ATM system depends not only upon the specific performance level of CNS systems, but also upon the performance of human operators who interact with the CNS system components. Therefore, the evaluation of specific levels of system performance requires an innovative structural framework that can evaluate overall emergent system performance arising from the behavior of individual system components including human operators. 2 |
