Air Traffic Noise Monitoring with ADS-B Signal and GIS

Abstract

The adverse impact of aviation noise on residents living near airports is a significant concern due to its high intensity and unpredictable nature. Prolonged exposure to aviation noise has been linked to various medical problems, including high blood pressure, ischaemic heart disease, diabetes, and poor sleep quality. Therefore, it is essential to continuously monitor the noise levels of airports to ensure compliance with local government and international regulations. Current monitoring techniques involve 2D noise mapping of the aircraft's runway, but this method has limitations in accurately assessing noise exposure in the surrounding areas. To address these limitations, this study proposes a novel approach to produce a 2D noise mapping simulation of airports through spatial and temporal analysis. This approach uses the Automatic Dependent Surveillance-Broadcast (ADS-B) signals, which provide precise and real-time location data of aircraft to air traffic control. By processing the ADS-B and ground station data using Python, this study aims to accurately monitor and predict noise levels in the vicinity of airports. The proposed approach has the potential to provide more accurate and comprehensive noise exposure data for assessing the impact of aviation noise on the health and well-being of residents living near airports. This study's findings may inform the development of noise mitigation strategies to reduce the impact of aviation noise on affected communities.

Date of Award	7 May 2024
Original language	American English
Supervisor	Tadahiro Kishida (Supervisor)