Band 13, Heft 1

Traffic noise is the common component at road intersections in the outdoor environment, and the sounds of traffic light signals are designed to distinguish signal transition between red and green. However, the sounds of traffic light signals are often masked by high levels of vehicle noise produced by high traffic flow. Therefore, this study aims to investigate the acoustic effects of traffic light signals and noise on sound sensitivity in the outdoor environment. A set of psychoacoustic indicators were applied to assess the traffic noise consisting of traffic light signals and vehicle noise at road intersections in Hong Kong. Furthermore, the subjective listening tests evaluated the acoustic sensitivity and perceptions. The results of psychoacoustic time-varying curves revealed distinct characteristics of traffic light signals, with fluctuation strength differing significantly between red and green signals. Moreover, the subjective perceptions of rapid tempo and regularity were significantly correlated with the measured fluctuation strength and rhythmic events per minute (REPM) during the signal transition. These findings will be the insight into the acoustic interactions between various sound sources and their perceptual implications in the outdoor environment.

Noise pollution is a growing threat to public health in dense cities yet widely used quietness indices were calibrated in European contexts and transfer poorly to Asian megacities. This study proposed the Adaptive Quietness Suitability Index (AQSI), a refined, validated framework that enhances sensitivity, accuracy, and transferability in mixed-use urban environments. The AQSI advances three elements: (i) dynamic impact-zone delineation via physics-based sound propagation rather than fixed buffers, (ii) alignment with locally regulated noise limits and control zones, and (iii) explicit temporal stratification (daytime, evening, nighttime, all-day) to capture diurnal variability. We validated the AQSI using monitoring data from 1997 to 2024 in New Taipei City, Taiwan, and compared it to the original QSI through regression and spatial analyses. The AQSI showed stronger agreement with measured sound levels than the original QSI (Pearson’s r  = −0.238 vs. −0.202), representing a 39 % improvement in explained variance ( R 2  = 0.057 vs. 0.041). It eliminated extreme-value saturation (0 or 1 in 86.2 % of cells) and reduced it to 0 %, yielding continuous gradients that better resolved intermediate environments. Nighttime emerged as the critical period, with the steepest negative coefficient (−0.0142, p  < 0.001) and the highest adjusted R 2 (0.16), highlighting the need for nocturnal noise management. By integrating operational mapping with soundscape-aware considerations at a 50 m resolution, the AQSI provides a transparent, context-sensitive tool for urban planning, regulatory compliance, and targeted mitigation in diverse metropolitan settings.

This study evaluates the impact of road traffic noise on roadside urban schools by adopting a dual methodological approach that combines quantitative noise monitoring and mapping with a socio-acoustical survey. 10 schools located near busy roads with varying noise exposure levels were examined. Noise levels were measured both inside classrooms and along adjacent roadways, and key acoustic indices including Traffic Noise Index (TNI), Noise Climate (NC), and Noise Pollution Level (L np ) were computed. Noise propagation maps were generated to visualize extent and spatial distribution of noise exposure around each school. Results show that observed noise levels frequently exceeded the World Health Organization (WHO) recommended limits, with exceedances ranging from 15 % in S5 to 107 % in S6. The socio-acoustical survey further highlighted the perceptual and psychological impacts of noise, including reduced speech intelligibility and increased emotional stress. Approximately 80 % of students reported frequent disturbance from external noise, 65 % experienced loss of concentration, and 50 % reported feelings of annoyance or fatigue. While the findings provide substantial evidence of the adverse effects of traffic noise on roadside schools, further research is needed to deepen understanding and explore long-term implications.

Road traffic noise is a significant environmental risk factor to the human health, resulting in the necessity for accurate noise prediction models with less uncertainty. Several standards and methods are available in commercial noise modeling software to predict noise levels from road traffic noise, such as ISO 9613-2, CNOSSOS-EU, RLS 19, NMPB2008, RMG2012 and Nord2000. However, many of these standards were developed for specific national conditions and thus have limitations in wider applications. For instance, the RLS 19 standard is most suitable for the German road network, the NMPB2008 standard is tailored to French conditions, the RMG2012 is Dutch and the Nord2000 standard is predominantly applied in Northern Europe. Similarly, the use of CNOSSOS-EU is limited to European Union member states that have transposed the method into national law, as recommended by the Environmental Noise Directive (END), due to varying road surface characteristics across countries. In contrast, the ISO 9613-2 standard, entitled “Acoustics – Attenuation of sound during propagation outdoors – Part 2: Engineering method for the prediction of sound pressure levels outdoors” provides a general engineering framework for estimating noise levels from several sources, such as road traffic noise. This paper presents the results of a thorough investigation in which predicted noise levels from ISO 9613-2 and CNOSSOS-EU models were compared with measured data for a road section with relatively low traffic volume. The purpose of this study is to identify the modeling approach that delivers the most realistic predictions, offering guidance on selecting appropriate methods and techniques for similar traffic conditions. The findings contribute to a better understanding of the applicability of the evaluated methods for noise modeling of roads with low traffic volumes, highlighting their respective strengths and limitations. This comparison can support environmental authorities and acoustic consultants in selecting the most suitable prediction method for local and national assessments, and it provides a basis for improving model calibration and adaptation in future studies.