UC Irvine

A comprehensive, data-driven methodology for analyzing aircraft performance factors frompublicly available operational radar and weather data is presented. This approach offers a versatile framework for assessing issues such as fuel burn, correlating operations with noise events, and gaining insights into airspace dynamics to support the integration of future air- craft concepts. One key application of this methodology is the evaluation of operational and environmental factors, along with their interactions, that contribute to noise variations across different aircraft types—a significant and growing challenge for airports. Aviation-induced community noise remains a persistent issue, and this methodology is driven by the need for operational adjustments to mitigate its effects. By identifying the sources and factors influencing noise propagation, informed modifications to flight procedures can be imple- mented. The methodology uses publicly available, ADS-B historical surveillance data from the Opensky Network and classifies departures from arrivals. It integrates noise monitoring recordings from the airport ground noise monitoring network with flight trajectories and collects weather data at the airport and noise monitor positions during specific timestamps using the NOAA Rapid Refresh (NOAA RAP) model. The methodology was applied to operational flight data from Seattle-Tacoma and Boston-Logan airports, leveraging several years of ADS-B data and noise monitor recordings for Airbus A319, A320, A321, Boeing 737-700, -800, -900, and Boeing 777-200LR/ER aircraft. Noise variations were analyzed as a function of observed parameters—including aircraft type, flight trajectory, airline, wind, temperature, pressure, and relative humidity—and inferred variables, such as aircraft config- uration, thrust, and weight. Spearman correlation matrices and gradient-boosting decision tree models were employed to evaluate the impact of these factors on noise variation. Other applications implementing this methodology include estimating fuel consumption by tracking weather along flight paths, assessing the feasibility of Advanced Air Mobility (AAM) oper- ations through visualizing current airspace organization, and developing speed and altitude profiles for various aircraft types.