Impact of Street Landscape Factors on Noise Exposure: An XGBoost - SHAP Modelling Approach

Abstract

Urban street sound exposure levels serve as critical physical indicators for assessing traffic noise pollution. While existing research has focused predominantly on source parameters such as traffic volume and speed, systematic investigations into the potential regulatory mechanisms of street landscapes remain limited. This study aims to quantify multidimensional street landscape factors and, employing an explainable machine learning approach, precisely decipher their impact degree and underlying mechanisms on noise exposure level (NEL). Through the collection of street view imagery and semantic segmentation techniques, morphological indicators, including the sky view factor, green view ratio, building interface height and continuity, and road width, were extracted. The daytime equivalent continuous A-weighted sound pressure level (LAeq) was used as the NEL metric. An XGBoost machine learning model was constructed, with street landscape factors as the core independent variables for NEL prediction. The SHapley additive exPlanations framework was incorporated to quantify the contribution of each factor, revealing nonlinear relationships and interaction effects with NEL. The results demonstrate the following findings. First of all, the XGBoost model achieved high predictive accuracy for NEL, outperforming traditional benchmark models. Secondly, street landscape factors exhibited significant explanatory power for NEL, with contribution levels comparable to those of some traditional source parameters.Additionally, pronounced nonlinear relationships were identified between key factors, such as building interface height and continuity. Lastly, interaction effects existed among street landscape factors, manifesting as synergistic noise reduction or antagonistic effects under specific combinations. This research deepens the understanding of urban sonic environment formation mechanisms both theoretically and methodologically. Theoretically, street landscape factors are key environmental variables regulating NEL, promoting a paradigm shift in noise research from “source management” to “environmental mediation”. Methodologically, the developed XGBoost-SHAP framework provides a powerful, explainable tool for addressing nonlinearity and interactions within complex urban systems. Practically, the findings offer a quantitative basis for implementing coordinated “source‒path” noise mitigation at the forefront of urban planning and design, translating abstract acoustic goals into precise spatial design language and advancing a data-driven preventive environmental governance model.

Keywords: Noise Exposure, Urban Street Factors, XGBoost Model, Cross-sensory Effects, Urban Design, Nonlinear Effects, Spatial Morphology, Urban Noise Mitigation

DOI: 10.54941/ahfe1007959