Mitigating noise pollution from road traffic is crucial in urban areas, and an effective solution is the use of trees as noise barriers in high-risk noise areas. This study proposes the incorporation of 3D noise visualisation within 3D building models to accurately visualise and quantify road traffic noise levels on building facades. Traffic noise propagates in all directions, necessitating the need for 3D visualisation. A selected tree belt in front of a three-storey building serves as the investigation site, where simulations compare noise levels along the facades with and without tree absorption. An equation is formulated to quantify road traffic noise levels and tree noise absorption. The study emphasizes the vital role of tree canopy in noise absorption, with properties such as leaf surface area, tree depth, and leaf frequency absorption being instrumental in determining the extent of noise reduction. To determine tree canopy properties, a LiDAR survey is employed, and convex hull visualisation of 3D trees is utilized to extract relevant properties. The study's results reveal that trees can absorb approximately 3 dB of noise levels. Overall, this research demonstrates the effectiveness of using trees as noise barriers and showcases the importance of 3D noise visualisation in understanding and mitigating road traffic noise on building facades.


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    Titel :

    Road Traffic Noise Pollution Mitigation Strategies Based on 3D Tree Modelling and Visualisation


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Ben Ahmed, Mohamed (Herausgeber:in) / Boudhir, Anouar Abdelhakim (Herausgeber:in) / El Meouche, Rani (Herausgeber:in) / Karaș, İsmail Rakıp (Herausgeber:in) / Wickramathilaka, Nevil (Autor:in) / Ujang, Uznir (Autor:in) / Azri, Suhaibah (Autor:in)

    Kongress:

    The Proceedings of the International Conference on Smart City Applications ; 2023 ; Paris, France October 04, 2023 - October 06, 2023



    Erscheinungsdatum :

    20.03.2024


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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