首页|Simulation and exposure assessment of hourly traffic noise in Hong Kong using a minimal error iterative model based on diversion strategies
Simulation and exposure assessment of hourly traffic noise in Hong Kong using a minimal error iterative model based on diversion strategies
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NETL
NSTL
Elsevier
Traffic noise poses a globally significant environmental threat to urban livability, particularly in high-density areas where conventional noise assessment methods struggle to capture dynamic spatio-temporal variations. The Minimal Error Iterative Model based on Diversion Strategies (MEI-DS) was proposed in this study to derive high-resolution traffic flow networks with overcoming temporal granularity limitations. A case study in Hong Kong, China, a high-density building environment city was conducted to examine the model performance, with an average relative error of 0.48 %. Afterwards, a novel noise assessment framework was developed by integrating MEI-DS-generated flows with noise source model and 3D noise propagation model. This approach reveals striking spatiotemporal heterogeneities: Peak noise levels occur between 08:00-09:00 on weekdays, while Saturdays show persistently high noise levels from 09:00 to 20:00. Sundays exhibit minimal diurnal noise fluctuations. Multi-scale assessments (city-district-building-individual) reveal 85.9 % of the population experiences noise exposure exceeding WHO-recommended thresholds. This study offers actionable insights to inform urban planning and develop health-centric strategies for mitigating traffic noise, and the proposed model can also be transferred to other regions with strong potential to address the impact of traffic noise on environmental health.
The Hong Kong Polytechnic University Department of Land Surveying and GeoInformatics
The Hong Kong Polytechnic University Department of Land Surveying and GeoInformatics||The Hong Kong Polytechnic University Research Institute for Sustainable Urban Development
The Chinese University of Hong Kong Department of Geography and Resources Management||The Chinese University of Hong Kong Institute of Space and Earth Information Science
NETL
NSTL
Elsevier
