Integrating ADS-B Data for Enhanced Airport Noise Modeling and Environmental Management

Although the COVID-19 crisis reduced the total number of people suffering from aviation noise, along with significant noise reduction achieved through the effective implementation of the ICAO Balanced Approach, aviation noise continues to be a sensitive environmental factor for local communities. Developing a comprehensive management strategy requires realistic input data for accurate noise modeling and management.

This study explores the integration of ADS-B data to improve aviation noise management and multicriteria optimization. The maximum entropy approach is applied to incorporate environmental and operational interrelationships, including noise criteria from various sources. This proposed methodology presents a holistic approach that unites aviation noise modeling, monitoring/measuring, and management to identify and substantiate the best noise reduction practices for a specific airport. The study specifically examines the sensitivity of noise modeling results to realistic flight tracks (ADS-B data) for urban airports in Ukraine, focusing on both lateral (approach and departure stages) and vertical dispersion. Test cases are outlined to demonstrate the efficiency of the entropy-based optimization model.