Algorithm development for aircraft noise prediction in urban environments

The outbreak of COVID-19 pandemic has led to the implementation of remote work as one of the safe management measures at workplaces. Consequently, most of the population has been staying home in the daytime—a period when the volume of air traffic may be higher. For residents staying near airport...

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Bibliographic Details
Main Author: Yong, Jian Rong
Other Authors: New Tze How, Daniel
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/166178
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Institution: Nanyang Technological University
Language: English
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Summary:The outbreak of COVID-19 pandemic has led to the implementation of remote work as one of the safe management measures at workplaces. Consequently, most of the population has been staying home in the daytime—a period when the volume of air traffic may be higher. For residents staying near airports, their work concentration may be adversely affected by aircraft noise and prolonged exposure could cause health problems. Hence, it is crucial to consider how aircraft noise can be predicted using noise maps for urban planners to create noise abatement measures. Before any noise management measures can be assessed for feasibility, developing a robust aircraft noise prediction algorithm is first necessary to unveil how residential areas are affected by aircraft noise. This paper presents an overview of the aircraft noise prediction capability that has been developed based on the standards in European Civil Aviation Conference Doc. 29 4 th Edition. The computation procedure makes use of flight path data from FlightRadar24, sound propagation physics as documented in Doc. 29, and noise measurements taken from Singapore Changi Airport. The scope of this paper is focussed on comparing noise parameters against that of SoundPlan, a commercial noise prediction software. The results are then compared against real-world measurements taken at Changi Airport. Results showed that the developed algorithm is able to predict the aircraft noise from a range of aircraft