There are many runway capacity estimation models currently available today, and developers usually claim that their models have been validated. However, information about the validation process is often limited, and different models are validated at different levels of complexity. As a result, this paper proposes two validation methodologies that can be used to test model predictions against reality. We demonstrate the methods on two model--the Airfield Capacity Model (ACM) and Runway Simulator (rS)--and two airports—SFO and LAX. The results indicate that both models tend to over-predict capacities under good visibility conditions, and predict wider ranges of capacities than are seen empirically. Overall, capacity estimates from rS are typically more accurate than those from ACM.
Theme: ATM Performance Measurement and Management
Keywords: ACM, capacity models, censored regression, empirical estimation, rS, runway capacity, validation
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