Visualising the Perception of Aircraft Noise for Communities Surrounding Airports

该项目的基本原理帮助利益相关者，如机场当局和当地社区，更好地了解空中交通的影响，并做出更明智的决定。例如，机场当局可以使用可视化来确定最需要采取噪音缓解措施的地区，例如Metasonixx使用超材料，并更好地优化飞机空间的使用，而当地社区可以使用可视化来倡导改变飞行路径或飞机运营。通过使用基于非声学因素的额外可视化来确定人们最受影响的地方，该项目可以提供关于飞机噪音影响的更完整的图片。提高认识和吸引公众：通过创建基于网络的软件，该项目可以帮助提高对飞机噪音影响的认识，并吸引公众讨论如何解决这个问题。形象化可以成为一种强有力的工具，以便于获取的方式传播复杂的信息，使公众更容易理解和参与解决问题。创建飞机噪音的可视化，以提供比目前更全面的了解飞机噪音对当地社区的影响。.研究并纳入其他指标，如感知、频率、飞机大小和高度，以更好地了解飞机噪音的影响。创建一个WebXR应用程序，使公众和航空业能够轻松访问可视化。使用机器学习技术来提高可视化中使用的数据的准确性，并使其更适用于没有噪音监测器的地点。.通过开发一种新的方法来可视化和了解飞机噪音的影响，为航空噪音研究领域做出贡献，该方法可供更广泛的受众使用。方法为了获得多个国际机场的飞机噪音、大小、感知和频率数据，我们将通过各种资源（如WebTrak）搜索可用的数据集。如果有关飞机噪音的数据不足够，我们可能需要使用噪音监察器搜集额外数据。非声学数据可以通过研究论文获得，例如“飞机高度和尺寸的声景评估”Gianluca Memoli [4]。不过，我们亦会进行调查及问卷调查，以取得进一步的非声学数据。为了处理数据，我们将使用机器学习技术来更准确地了解不同地点的飞机噪音水平。通过数据分析获得的信息将用于生成图形可视化，该图形可视化将使用Unity或虚幻引擎开发为应用程序。这种图形化可视化将通过WebXR应用程序提供给公众和行业。预期成果：飞机噪音对当地社区影响的全面可视化，包括非声学因素，公众和航空业可以通过网络应用程序访问。使用机器学习技术提高飞机噪声数据的准确性和适用性。一种新的改进方法，用于可视化和了解飞机噪音的影响，使机场当局能够最大限度地利用空域并最大限度地减少抗议活动。通过提供对飞机噪声影响的更全面的了解，为航空噪声研究领域做出贡献，利益相关者可以利用这些信息做出明智的决策。

Rationale for the ProjectTo help stakeholders, such as airport authorities and local communities, to better understand the impact of aircraftoise and make more informed decisions. For example, airport authorities could use the visualisations to identifyreas where noise mitigation measures such as the use of metamaterials by Metasonixx are most needed and to better optimise the use of aircraft space, while local communities could use the visualisations to advocate for changes to flight paths or aircraft operations.To provide a more comprehensive understanding of the impact of aircraft noise in relation to local communities. By using additional visualisations based upon non-acoustic factors to identify where people are most impacted, the project could provide a more complete picture on the impact of aircraft noise.To raise awareness and engage the public: By creating web-based software, the project could help raise awareness of the impact of aircraft noise and engage the public in discussions about how to address the issue. Visualisations can be a powerful tool for communicating complex information in an accessible way, making it easier for the public to understand and engage with the issue.Aims and objectives:. To create a visualisation of aircraft noise that provides a more comprehensive understanding of the impact of aircraft noise to local communities than is currently available. . To research and incorporate additional metrics, such as perception, frequency, aircraft size and height, to gain a better understanding of the effects of aircraft noise . To create a WebXR application that enables the public and aviation industry to access the visualisation easily . To use machine learning techniques to improve the accuracy of the data used in the visualisation and to make it more applicable to locations without noise monitors. . To contribute to the field of aviation noise research by developing a new method for visualising and understanding the impact of aircraft noise that is accessible to a wider audience. Methodology To obtain data on aircraft noise, size, perception and frequency at multiple international airports, we will search for available datasets through various resources such as WebTrak [8]. If the available data on aircraft noise is inadequate, we may need to source additional data using noise monitors. Non-acoustic data can be obtained through research papers such as the "Soundscape Assessment of Aircraft Height and Size" Gianluca Memoli [4]. However, we will also conduct surveys and questionnaires to obtain further non-acoustic data. To process the data, we will use machine learning techniques to obtain a more precise understanding of aircraft noise levels in various locations. The information obtained through data analysis will then be utilized to generate a graphical visualisation, which will be developed as an application using either Unity or Unreal engine. This graphical visualisation will then be made available to the public and industry through a WebXR application. Expected Outcome: . A comprehensive visualisation of the impact of aircraft noise on local communities, incorporating non-acoustic factors, which can be accessed through a web application by the public and aviation industry . Improved accuracy and applicability of aircraft noise data using machine learning techniques . A new improved method for visualising and understanding the impact of aircraft noise that allows the airport authorities to maximise the use of airspace and minimise protests . A contribution to the field of aviation noise research by providing a more comprehensive understanding of the impact of aircraft noise, which can be used by stakeholders to make informed decisions.