Continuous Descent Approach (CDA), also known as Optimized Profile Descent (OPD), is the advanced flight technique for commercial aircraft to descend continuously from cruise altitude to Final Approach Fix (FAF) or touchdown without level-offs and with- or near-idle thrust setting. Descending using CDA, aircraft stays as high as possible for longer time thereby expanding the vertical distance between aircraft's sources of noise and ground, and thus significantly reducing the noise levels for populated areas around airports. Also, descending with idle engines, fuel burn is reduced resulting in reduction of harmful emissions to the environment and fuel consumption to air carriers. Due to safety considerations, CDA procedures may require more separation between aircraft, which could reduce the full utilization of runway capacity. Thus, CDA has been limited to low to moderate traffic levels at airports. Several studies in literature have used various approaches to present solutions to the problem of increasing the CDA implementation during periods of high traffic at airports. However, insufficient attention was given to define thresholds that would help Air Traffic Controllers (ATC) to manage and accommodate more CDA operations, strategically and tactically. Bridging this gap is the main intent of this work.
This research focus is on increasing CDA operations at airports during high traffic levels by considering factors that impact its CDA adoption as they relate to airports' demographics, and airspace around them {known as terminal maneuvering area (TMA)}. To capture the effect of these factors on CDA Adoptability (CDA-A), in general, and CDA Predictability (CDA-P), at the operational level, two (2) approaches are introduced. The CDA-A model defines and captures the maximum level of traffic threshold for CDA adoption. The model captures the factors affecting CDA in a single measure, which are designated collectively as the Probability of Blocking. It is defined as the fraction of time an aircraft's request to embark on CDA is denied. The denial could emanate from safety concerns as well as other operational conditions, such as the congestion of the stacking space within the TMA. This metric should enhance ATC on the strategic level to increasing CDA operations during possibly higher traffic than normally the case. The other approach is for a CDA-P. This model is developed based on data-driven system approach. It extracts traffic features, such as aircraft type and speed, altitude, and rate of descent; from actual flights data to aid in further operational utilization of CDA in real time. By accurately predicting CDA instances during high traffic at airports, the CDA-P model should assist ATC manage adopting more CDA operations during periods of high demand. Through its framework, the CDA-P model utilizes Feature Engineering and Hierarchal Clustering Analysis, to facilitate descent profile visualization and labeling, for building, training, testing, and validation of CDA predictive models using Decision Trees with AdaBoost and Support Vector Machines (SVM). The CDA-P model is validated using actual flight data operated at Nashville Int'l Airport (BNA).
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