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Article

Impact of Urban Elevated Complex Roads on Acoustic Environment Quality in Adjacent Areas: A Field Measurement Study

by
Guangrui Yang
1,
Lingshan He
2,
Yimin Wang
1,* and
Qilin Liu
3
1
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
2
Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd., Guangzhou 510060, China
3
School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(15), 2662; https://doi.org/10.3390/buildings15152662
Submission received: 9 June 2025 / Revised: 16 July 2025 / Accepted: 22 July 2025 / Published: 28 July 2025
(This article belongs to the Special Issue Vibration Prediction and Noise Assessment of Building Structures)
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Abstract

The current focus of urban environmental governance is on the traffic noise pollution caused by road transportation. Elevated complex roads, defined as transportation systems comprising elevated roads and underlying ground-level roads, exhibit unique traffic noise distribution characteristics due to the presence of double-decked roads and viaducts. This study conducted noise measurements at two sections of elevated complex roads in Guangzhou, including assessing noise levels at the road boundaries and examining noise distribution at different distances from roads and building heights. The results show that the horizontal distance attenuation of noise in adjacent areas exhibits no significant difference from that of ground-level roads, but substantial discrepancies exist in vertical height distribution. The under-viaduct space experiences more severe noise pollution than areas above the viaduct height, and the installation of sound barriers alters the spatial distribution trend of traffic noise. Given that installing sound barriers solely on elevated roads is insufficient to improve the acoustic environment, systematic noise mitigation strategies should be developed for elevated composite road systems. Additionally, the study reveals that nighttime noise fluctuations are significantly greater than those during the day, further exacerbating residents’ noise annoyance.

1. Introduction

Noise pollution has emerged as one of the most pressing environmental issues attributable to road traffic [1]. Recent research in the field of modern medicine has unequivocally established that traffic noise originating from urban roads poses a substantial risk to human health [2,3]. The primary threats associated with traffic noise include an increased risk of cardiovascular diseases [4,5,6], diabetes, and hypertension [7], as well as the development of depression [8,9], sleep disorders [10,11], and adverse effects on children’s cognitive function [12,13]. Furthermore, recent studies have also established a link between road traffic noise and the prevalence of obesity [14].
With the development of cities, the relentless expansion of road networks, aimed at accommodating the increasing volume of traffic, has led to a substantial increase in noise levels in many urban areas, severely disrupting residents’ daily lives [15,16,17]. Especially in densely populated Asian cities, the high density of traffic and towering building patterns make traffic noise a potentially more significant disturbance for residents. Using three-dimensional technology and the ISO 9613-2 method, Brown et al. [18] conducted a traffic noise simulation across Hong Kong. The findings indicated that the percentage of the Hong Kong population exposed to noise levels exceeding 70 dB(A) was comparable to that in Europe. However, in comparison to European cities, a significantly higher percentage of the Hong Kong population was found to be exposed to noise levels between 60 dB(A) and 64 dB(A). Consequently, as the primary source of noise in urban areas [19], road traffic noise has attracted significant attention from city managers.
Accurate assessment of citizens’ noise exposure is essential to inform evidence-based decision-making [20]. To this end, countries including the European Union, the United Kingdom, the United States, China, and Germany have successively introduced noise prediction models. These models incorporate both emission and propagation modules, enabling researchers to leverage non-acoustic data to forecast the spatial distribution of road traffic noise [21]. They have been validated for application in urban road networks, yet the noise sources of the validation datasets are predominantly from surface roads that are consistent with the road types utilized in model development [22,23,24,25,26]. The study by Melo et al. [27] proposes that the road noise prediction model is suitable for estimating urban roads with similar traffic characteristics and road layouts. In addition, the spatial structure of buildings in high-density cities also influences the propagation and distribution of noise [28]. Huang et al. [29] conducted field tests on noise in high-rise buildings along highways and discovered that the FHWA model underestimated the height of the maximum noise level on high-rise buildings compared to actual conditions. Therefore, conducting on-site tests for traffic noise in typical areas with special road types and exploring the distribution of noise, is significant for scientifically assessing the impact of traffic noise in high-density urban and designing noise reduction measures.
Elevated roads represent the primary form of urban transportation expansion aimed at enhancing traffic efficiency. In major cities, elevated roads are often constructed above ground-level roads parallel to their alignment, thus forming an elevated complex road system. The complex arrangement of double-story noise emission sources and viaducts gives rise to alterations in traffic noise propagation patterns [30]. However, no studies have systematically addressed noise propagation in elevated complex road systems. Furthermore, noise barriers are effective noise reduction measures for elevated roads [31], but their application in elevated complex roads still lacks post-evaluation.
The main objective of this study is to deeply investigate the impact of traffic noise generated by elevated complex roads on the sound field distribution in adjacent areas through field tests and to assess the effectiveness of noise mitigation measures. Additionally, this study discusses the temporal variations in traffic noise during different periods of urban traffic operation. Through this study, we hope to offer valuable insights into noise monitoring assessment and reduction strategies for existing road structures of this kind, while also serving as a reference for the construction of elevated roads in other regions, particularly in emerging countries that are experiencing rapid growth in motor vehicle traffic and urban development.

2. Methods

2.1. Location

The analysis data was collected in the vicinity of elevated complex roads in Guangzhou. The city boasts a permanent resident population density of 2532 individuals per square kilometer, with the central urban area reaching a staggering 22,592 people per square kilometer. Alongside this dense population, Guangzhou has constructed an extensive network of 15,017 km of roads and 2234 viaducts [32]. This robust road transportation system, coupled with the dense population distribution, has significantly increased the exposure risk of citizens to traffic noise [33,34,35].
In the urban core of Guangzhou, two elevated complex road sections were selected as test areas, Test Area #1 and Test Area #2. The selection criteria for the test area are as follows: the primary noise source in the area is road noise generated by traffic flow on elevated complex roads, and the area contains a high-rise building that is affected by noise interference. Additionally, there are no other obvious obstacles between the building and the road except for the sound barrier to be studied.
The ground-level road under the viaduct in Test Area #1 was built in 1974 and serves as a key arterial road. It has four lanes in both directions (17 m wide), serving as a vital link between districts and primarily fulfilling traffic functions. The elevated road, the first urban viaduct in Mainland China, was built in 1987 and stands 5.5 m high. It has two lanes in both directions (9 m wide). Measurement points are set at the junction of the ramp and the main road, where the ramp is 4 m wide.
To analyze the impact of traffic noise at different heights, a road-facing building was selected for noise measurement in each test area: Building #1 (for Test Area #1) and Building #2 (for Test Area #2). Building #1 is a six-story residential building (2.6 m per floor). Floors 1 and 2 are below the viaduct, floors 4 to 6 are above it, and floor 3 is level with the viaduct structure. Building #1 is 7 m from the ground-level road and 3 m from the elevated road. An overview of Test Area #1 is shown in Figure 1.
The ground-level road of Test Area #2 is categorized as an arterial road in the urban road hierarchy. It has eight lanes in both directions and is 33 m wide. The elevated road above is an expressway, designed to ensure smooth traffic flow and enhance urban transportation efficiency. The expressway has eight lanes in both directions (32 m wide), with 3-meter-high arc-shaped top vertical noise barriers on both sides. The viaduct in Test Area #2 is a separate elevated structure, with varying heights for different driving directions: 14 m for east-to-west traffic and 10 m for west-to-east. The road surfaces in both test areas are paved with asphalt.
Building #2 is an eight-story commercial hotel. The first floor is 3.5 m high, and the remaining floors are 2.8 m each. Floors 1 to 5 are located below the viaduct, floors 7 and 8 are above the viaduct, and floor 6 is at the same elevation as the viaduct structure. Building #2 is 8 m from the ground-level road and 7 m from the elevated road. An overview of Test Area #2 is shown in Figure 2.

2.2. Measurement Scheme

Equipment used in the measurement process includes the RION NL-52A (precision sound level meter for noise signal acquisition, manufactured by RION Co., Ltd., Tokyo, Japan) and the RION NC-75 (acoustic calibrator for calibrating precision sound level meters, manufactured by RION Co., Ltd., Tokyo, Japan). They fulfill the requirements of the International Electrotechnical Commission for Class 1 sound level meters (IEC 61672-1:2013) and Class 1 sound calibrators (IEC 60942:2017).
A total of 19 measurement points were deployed across the two test areas to monitor traffic noise levels at varying distances from the roads and different building heights, aiming to analyze the noise attenuation characteristics with distance and variation patterns with height. In Test Area #1, five points were positioned at 0.2 m, 10 m, 20 m, 40 m, and 60 m from the ground-level road, while another five points were set up on the 1st, 2nd, 3rd, 4th, and 6th floors of Building #1. In Test Area #2, four points were arranged at 0.2 m, 5 m, 10 m, and 20 m from the ground-level road, with five additional points on the 1st, 3rd, 4th, 6th, and 8th floors of Building #2. Except for the noise barrier in Test Area #2, no obstructions exist between the measurement points and the roads, or between the measurement points themselves, that could impede noise propagation. It should be noted that the points on Building #1 were located on the road-facing facade, whereas those on Building #2 were positioned outside the side windows, 12 m from the ground-level road and 13 m from the elevated road. During the measurements, the sound level meter was mounted on 1.5 m high tripods, with a 1 m clearance from walls or windows. The microphone was oriented towards the road. The layout of the measurement points is shown in Figure 3.
To reflect the impact of traffic flow variations on noise, measurements were conducted in each test area across three time periods: 14:30–16:00 (Period 1, daytime off-peak), 17:30–19:00 (Period 2, daytime peak), and 22:00–23:30 (Period 3, nighttime). Measurements were performed in February–March under clear weather (no rain, snow, thunder, or lightning), with temperatures ranging from 20.4 °C to 27.3 °C, humidity between 40% and 68%, and wind speeds below 2.5 m/s.
Continuous measurements are critical in traffic noise studies. In accordance with Chinese standards, each measurement session lasted 20 min to capture noise levels and fluctuations under stable traffic flow conditions [36]. To ensure simultaneous measurements at all points reflecting noise distribution, multiple operators each controlled an instrument. Additionally, warning signs were placed outside the test areas to exclude construction noise, human activity noise, and other extraneous noise sources that could affect results.
The equivalent continuous A-weighted sound pressure level (LAeq), maximum sound level (Lmax), percentile sound levels (L10, L50, L90), and spectral information (obtained through 1/3 octave band analysis) are discussed in this study. These indicators were derived by analyzing the 20 min WAV files collected at each measurement point using the Waveform Analysis Software AS-70 (Version 2.2.0.19) developed by RION Corporation.
Videos were recorded simultaneously during the noise measurement to statistically analyze the traffic flow characteristics within the measurement period.

3. Results

3.1. Characteristics of Traffic Noise in Urban Actual Traffic Operations

The temporal variation and frequency components of road traffic noise are analyzed in this section. Table 1 presents the acoustic indicators of noise measured at 20 cm from the road boundary. The variation in noise levels over time is illustrated in Figure 4, where the time-domain graph is plotted based on the sound pressure levels (SPLs) recorded at 100-millisecond intervals.
Road noise, characterized by significant fluctuation, stands out as a non-steady source of disturbance. Notably, in both test areas, the noise level fluctuation during period 3 exceeds that of periods 1 and 2, and there are reasons for this variation. In Test Area 1, the nighttime background noise level was lower, and the reduction in traffic flow (from 536 in Period 1 and 272 in Period 2 to 224 veh/20min in Period 3) rendered the noise generated by passing vehicles more prominent. This suggests that, even if the LAeq of nighttime noise is lower, the more pronounced fluctuation may still cause greater annoyance to people [37]. Conversely, in Test Area #2, although the total traffic volume at night decreased compared to daytime (from 1101 in Period 1 and 1012 in Period 2 to 716 in Period 3), the noise fluctuation and level at night were higher. This is because large vehicles with a loading capacity of over 7 tons, which generate significantly louder noise, saw a dramatic increase at night (from 5 in Periods 1 and 2 to 134 veh/20min in Period 3). This surge was due to local government policies prohibiting large vehicles from entering the urban area during the day, leading to their concentrated operation at night. From the perspective of noise indicators, this fluctuation level can be reflected by the difference between the L90 and L10 values. In test area #1, the daytime noise difference ranged from 6.7 to 5.9 dB(A), whereas during the night, this difference significantly escalated to 11.2 dB(A). Similarly, in test area #2, the daytime noise fluctuation spanned from 4.6 to 4.2 dB(A), yet at night, it witnessed a rise to 7.9 dB(A).
Vehicle horn honking is the main cause of noise peaks, with honking noise levels between 80.2 dB(A) and 98.6 dB(A). Additionally, pneumatic braking of large vehicles can also cause sudden increases in sound pressure levels.
The frequency distribution of traffic noise over the 20-minute duration is shown in Figure 5. To identify the main frequency ranges of the noise, it is necessary to analyze the relative energy contribution of each frequency component, so the data were first normalized. Equation (1) is the formula for SPL superposition based on the principle of energy superposition, which can calculate the total equivalent SPL (Leq) (i.e., the result of energy superposition in the frequency domain) by superimposing the equivalent SPLs (Ln) of each frequency component over the 20 min. Conversely, using Equation (2), the proportion of energy of each frequency in the total energy (Pn) can be calculated based on the total Leq and the Ln of each frequency, and these calculation results are further illustrated in Figure 5.
L eq = 10 lg ∑ 10 L n / 10
P n = 10 L n / 10 / 10 L eq / 10 × 100 %
The frequency distribution of traffic noise at the roadside beneath elevated complex road sections closely matches that observed in other urban road types [38]. When assessed under A-weighting conditions, traffic noise predominantly falls within the frequency range of 250 to 4000 Hz, with each frequency contributing over 1% to the total sound pressure level. Notably, a peak in noise is evident at 1000 Hz, making a significant contribution to the total sound pressure level, accounting for a range of 13.6% to 20.3%. The increase in test area #1 during period 3 at 6300 Hz was attributed to sharp noises emitted by bicycle brakes near the testing site.
The low-frequency noise characteristics in the two test areas exhibit differences. Specifically, in test area #2, the sound pressure level contributions within the frequency range of 12.5 to 125 Hz are noticeably higher compared to test area #1. Additionally, during period 3 in test area #2, there is a significant increase in sound pressure level contributions in the frequency range of 12.5 to 500 Hz compared to periods 1 and 2. Conversely, the contributions in the higher frequency range of 6300 to 20,000 Hz are lower during period 3 compared to the earlier periods. The increasing number of large vehicles has resulted in a growth in low-frequency components of traffic noise, a change that is noticeable to human ears. This growth is likely attributed to the more prominent impact of powertrain and structural noises originating from large vehicles, compared to small vehicles.

3.2. Noise Distance Attenuation Under Elevated Complex Roads

Figure 6 illustrates the decrement in traffic noise LAeq values as the distance from the road escalates. The road boundary serves as the baseline reference point. The specific LAeq values obtained from the measurements are provided in Appendix A, including the data used in Section 3.2 and Section 3.3. Given the prevalence of daytime human activities, it is impractical to ensure the absence of additional noise sources in the vicinity of the measurement points, which can lead to the contamination of certain measurement outcomes. Specifically, the readings recorded in Test Area #1 during period 2, precisely at 40 m and 60 m away from the road, were deemed anomalous and excluded from further analyses.
Equation (3) serves as the prediction model for noise distance attenuation, specified in China’s acoustic environmental guidelines, which applies to hourly traffic volumes of 300 vehicles or more per hour. The model is adapted from the FHWA prediction model, and its coefficients are calibrated based on Chinese measured data [39]. The horizontal distance between the reference point used in this equation and the noise source is 7.5 m, and ‘r’ denotes the distance from the prediction point to the noise source. In practical use, the centerline of the outermost lane is typically taken as the noise emission line. Given that Chinese urban arterial roads and expressways often have lane widths of 3.5 m or 3.75 m, the position 5 m away from the road is approximately considered the reference point. According to the measured data, traffic noise attenuates by approximately 1 dB(A) at this 5 m distance. Therefore, when comparing the predicted values of Equation (3) with measured attenuation values at other distances, a correction of 1 dB(A) is applied, as indicated by the red dashed line in Figure 6.
Δ D = 10 lg ( 7.5 / r )
Table 2 presents the differences between the actual noise distance attenuation values shown in Figure 6 and the predicted values from Equation (3). The results indicate that within 20 m, the difference is less than 2 dB(A); within 40 m, the difference is less than 3 dB(A). Therefore, Equation (3) can meet the requirements for predicting the distance attenuation of traffic noise under elevated complex roads, and it is more accurate at shorter distances.
The measurement results of period 3 were utilized to conduct a spectral analysis, from which the corresponding attenuation values were calculated (as shown in Figure 7). In the primary frequency range of traffic noise, spanning from 250 to 4000 Hz, the observed attenuation values across various frequencies generally align with those of the sound pressure level. Overall, the rate of attenuation is faster in the high-frequency range compared to the low-frequency range.

3.3. The Variation in Traffic Noise with Height

As depicted in Figure 8, the distribution of traffic noise for Building #1 and Building #2 reveals significant noise impacts, particularly during nighttime. Building #1 surpasses the standard limit (70 dB(A) daytime and 55 dB(A) nighttime [36]) by a range of 9.8 to 12.3 dB(A), whereas Building #2 exceeds it by a margin of 18.8 to 20.7 dB(A). Notably, despite the installation of noise barriers, noise levels at heights above the viaduct for building #2 still exceed the limit, suggesting the need for more effective noise reduction measures.
The pattern of traffic noise distribution differs between the two buildings, which is related to the height of the viaduct and the arrangement of noise barriers. Specifically, for building #1, the traffic noise diminishes initially as one ascends the floors, reaching its nadir at the level corresponding to the viaduct’s height. Then, as the floor level continues to rise, the noise level begins to increase. Conversely, building #2 experiences a rise and subsequent decline in traffic noise with increasing floor levels, peaking at the third floor.
Extensive research has concurred that traffic noise exhibits a trend of initial increase followed by a decrease with ascending height [40], stemming from the ground effect and variations in the propagation medium. The variation in noise levels above building #1’s viaduct indicates that this pattern is not limited to ground-level roads, but also extends to the noise generated by vehicles on elevated roads. At the same time, the test results for building #2 indicate that the configuration of noise barriers has modified the pattern of traffic noise’s variation with height. The specific causes of this phenomenon will be elaborated in Section 4.2 of the subsequent discussion.
Changes in noise levels within the under-viaduct height range are also noteworthy. Contrary to the expected trend where noise level increases with proximity to the elevated road, the mid-height under the viaduct experiences more intense traffic noise. This is partly because the noise from the ground-level road, as previously mentioned, first increases with height, and partly due to the noise reflection from the viaduct underside.
Figure 9 exhibits the frequency distribution of traffic noise across various heights during period 3. Within the primary frequency spectrum of traffic noise, ranging from 250 to 4000 Hz, the SPL variations across different frequency bands exhibit a consistent height-dependent pattern with the LAeq trend in Figure 8. Additionally, in contrast to the bridge-related low-frequency noise phenomena studied by other researchers [41,42], floors at the same elevation as the viaduct structure do not exhibit a significant rise in low-frequency noise. The lack of increase in emissions can be attributed to the relatively lower driving speeds and the lightweight characteristics of vehicles traveling on urban roads.

4. Discussion

4.1. How Can We Better Address Residential Noise Nuisance at Night?

According to the complaint results of the Guangzhou Municipal Transportation Bureau, complaints about traffic noise are concentrated during the period from 22:00 to 7:00. The residents’ desire for a low-noise environment during their nighttime rest and the implementation of stricter nighttime noise limits are considered as contributing factors to the concentration of complaints at night. However, our measurement data may provide an alternative explanation. Despite the reduced traffic volume at night, the sound pressure level from passing vehicles remains largely unchanged. Consequently, with a decreased level of background noise, traffic noise with similar daytime sound pressure levels becomes more noticeable. This significant fluctuation in noise is more likely to annoy residents, reduce sleep quality, and even affect children’s cognitive development [37,43,44]. One possible solution is to enforce different speed limits during the day and night, as vehicle speed significantly affects the noise level generated by passing vehicles [45]. This approach, however, requires further research by considering factors such as road capacity and traffic flow, to ensure that traffic noise is reduced while maintaining smooth and safe road operations.
To ensure the safety and smooth flow of urban road traffic, many Chinese cities designate areas and time restrictions for the passage of trucks in urban areas. The periods for prohibiting truck traffic are usually during morning and evening rush hours or the daytime. However, this approach has detrimentally impacted the nighttime acoustic environment. In test area #2, although the number of small vehicles has halved at night, the number of large vehicles soared from 5 to 134 veh/20 min, resulting in a significant increase in nighttime noise levels by between 2.1 and 3.9 dB(A). In addition, more obvious low-frequency noise has been found at night when the number of large vehicles increases. Compared with medium and high-frequency noise, low-frequency noise is easier to propagate, and it is more likely to make residents feel fatigued, dizzy, emotionally volatile, and other discomforts, aggravate the annoyance suffered by residents, and reduce speech clarity [46,47,48].
Therefore, due to the high noise level of large vehicles, their tendency to generate low-frequency noise, and the more pronounced noise fluctuations they cause, urban freight traffic planning needs to consider traffic noise and its interference with residential areas. It is essential to demarcate the scope of densely populated residential areas, as this can maximize the avoidance of affecting residents when designing truck-prohibited routes. Freight terminals and distribution loading/unloading points with heavy truck traffic are recommended to be located away from densely populated residential areas, and special freight corridors should be designed. Ma et al. [49] designed a lane restriction system to separate trucks from passenger vehicles, which is considered to have better traffic efficiency and reduce environmental costs. Although this system does not take noise into account, it can provide a design idea for freight corridors. Emerging technologies can be used to supplement cargo transportation in urban areas and reduce noise impacts, such as combining trucks with drones for package delivery [50,51].
Furthermore, as trucks travel at lower speeds in urban areas, their powertrain noise becomes the primary noise source. This makes the noise advantage of electric trucks even more prominent. Under low-speed conditions below 50 km/h, the main noise source of electric trucks is tire/road noise, in contrast to traditional diesel trucks. Electric trucks have the potential to significantly reduce noise by over 8 dB(A) [52]. Currently, new energy vehicles (NEVs) are developing rapidly, but their applications remain primarily focused on small and medium-sized passenger cars and public transport. There is still a need for technological R&D and policy formulation to promote the upgrading and replacement of conventional trucks.

4.2. Improvements Needed for Noise Prediction Methods in Adjacent Areas of Elevated Complex Roads

The construction of elevated roads is an important infrastructure measure to promote urban economic growth, but it may have adverse impacts on residents’ quality of life [53]. Hadi Baaj et al. [54] used the FHWA prediction model to forecast the noise impact of elevated complex roads. The results showed that the newly built elevated roads would increase vehicle speeds, thus causing greater noise pollution. Notably, this study only considered the noise levels at ground height (1.5 m above the ground), without accounting for the variation in noise with height. Wu et al. [55] used the CadnaA software to predict the three-dimensional distribution of noise from individual elevated roads and the influence of village morphology on noise propagation. For the three-dimensional distribution of pollutants in the vicinity of elevated complex roads, Lu et al. [56] analyzed the diffusion patterns of air pollutants such as PM1 and CO based on measured data and found a bimodal distribution in the vertical profile. Nonetheless, this study did not focus on noise distribution, and our study fills this gap.
In Section 3.2, the study compared the differences between the measured and predicted values of distance attenuation for road traffic noise on the horizontal plane. Within a range of 40 m, existing prediction methods can meet practical needs with an error within 3 dB(A). However, for the noise levels at different floors of buildings, the existing road noise prediction models struggle to accurately predict the vertical distribution of noise in the areas adjacent to elevated complex roads.
In China’s road noise prediction model, the noise levels at prediction points are obtained by superimposing the calculation results of the two roads, without accounting for noise reflection from the overpass bottom, which leads to inaccurate predictions. This is exemplified by our simulation using CadnaA (Version 2021 MR1) software for double-deck road noise distribution: the predicted noise level in the under-viaduct space increased continuously with height, which is inconsistent with the noise results shown in Figure 8. Noise reflection is a complex propagation process, where the viaduct height (height of the reflection surface), the width of the ground-level road (distribution of sound sources), the relative position between the viaduct and the ground-level road (relative position between the reflection surface and sound sources), and the distance between the building and the viaduct/ground-level road all affect the noise distribution on the building. Although our survey results did not provide a specific calculation method, they proved that the maximum noise height appears in the middle of the under-viaduct height, rather than increasing continuously with height.
Another factor to consider is that for the above-viaduct space of elevated complex roads without sound barriers, road noise first surges and then continues to increase slightly with height, as shown in the test results of the 3rd, 4th, and 6th floors in Figure 8a. The initial surge is due to the influence of traffic on the viaduct, while the subsequent slight increase occurs because the guardrails on both sides of the elevated road are higher than the equivalent sound source height of small vehicles, acting as a shorter sound barrier—particularly in this case, plant decorations on the guardrails further enhance the obstructive effect. According to the Huygens–Fresnel principle, the diffraction sound attenuation is related to the sound path difference. The larger the sound path difference, the greater the Fresnel number (N), and the more significant the attenuation effect. Therefore, as the height increases, the attenuation effect of the guardrails gradually decreases, and the noise level gradually increases.
Conversely, in the area with sound barriers, most of the noise in the above-viaduct space comes from the ground-level road. Due to the obstruction of the viaduct, the noise on higher floors is more significantly reduced, as shown with the 6th and 8th floors in Figure 8b. However, it is worth noting that although the height difference between the 8th and 6th floors is 6 m, the noise level difference between them is less than 0.5 dB(A). This is because, as analyzed earlier regarding the impact of sound path differences on barrier attenuation, higher floors, while experiencing less noise from ground-level roads, are subjected to more severe noise from elevated roads. Therefore, given that all measurement points were located within the acoustic shadow zones created by these barriers due to the limitation of floor height, the noise distribution at higher positions requires further investigation.
In summary, to better predict the noise distribution in the areas adjacent to elevated complex roads, it is necessary to improve existing noise prediction models by incorporating considerations of noise reflection from viaducts and the impact of guardrails.

4.3. Comprehensive Design of Multiple Noise Reduction Measures Needed

In major urban areas, noise reduction design is prioritized for the elevated sections of complex roads, as opposed to ground-level roads. So far, Guangzhou’s primary urban elevated roads have installed 65,551 m of noise barriers. However, a sole focus on noise reduction design for elevated roads alone may not result in the anticipated reduction in noise levels.
Our test data reveals that despite the presence of noise barriers, the noise level on the road-facing facade of the building still surpasses 70 dB(A). The inadequacy of the barriers’ noise reduction capabilities is a contributing factor, but the impact of the roads beneath viaducts also merits consideration. As traffic noise from ground-level roads rises upwards, it is influenced by the viaducts, causing a portion to reflect to the ground, thereby elevating noise levels beneath the viaducts. The maximum noise levels may be recorded within this height range. Meanwhile, the remaining noise continues to propagate upward, affecting floors higher up. Consequently, the design of noise reduction measures for ground-level roads must be taken seriously.
Low-noise asphalt pavement is widely utilized as a noise reduction measure. Practical experience in Guangzhou has confirmed the conclusions of many studies [57,58,59,60]. This type of pavement can initially reduce traffic noise by 3 to 8 dB(A) compared to traditional asphalt pavement and by over 10 dB(A) compared to cement concrete pavement. Furthermore, some innovative concrete materials (such as foamed concrete and porous concrete) have been developed [61], which have excellent sound absorption effects. A comprehensive noise reduction design for the under-viaduct space that can be adopted is to use low-noise asphalt pavement on the ground-level road to reduce the sound source level while using sound-absorbing concrete at the bottom of the bridge to reduce noise reflection. Notably, the utilization of solid waste has become a key development direction for emerging road surfaces [62,63]. The development of low-noise pavement materials using recycled materials will further promote the sustainable development of cities [64,65].
For areas with severe noise exceeding standards, noise reduction measures at the receiver (buildings) should also be considered. Acoustic treatment of building facades can effectively reduce indoor noise levels [66]. Naish et al. [67] proposed a design guide to optimize the acoustic treatment of balconies. Eggenschwiler et al. [68] found that using absorptive facades can reduce indoor noise levels, thereby decreasing residents’ annoyance. Although slightly rotating the building orientation has a limited noise reduction effect, it can significantly reduce noise interference. In addition, some plant treatment methods applied to building facades can mitigate the impact of road traffic on the acoustic environment [69].
Based on the measured results, this section discusses the comprehensive noise reduction design for elevated complex roads. In addition to road traffic, the subway system is also an important component of urban three-dimensional transportation. Similar to the noise problems caused by roads, the vibration and structure-borne noise problems caused by subways also annoy residents, and some studies are dedicated to the prediction and control of these problems [70,71].

5. Conclusions

This study conducts a comprehensive analysis of the distribution and temporal variations in traffic noise in areas adjacent to elevated complex roads, based on field measurements, and the following conclusions were drawn:
  • The distance attenuation of noise from elevated complex roads at ground level is not significantly different from that of general roads. By comparing the measured data with the predicted data, it is demonstrated that the distance attenuation formulas of the Chinese Sound Environment Guidelines and the US FHWA Road Noise Prediction Model remain applicable to elevated complex roads.
  • Given the presence of noise barriers on many elevated roads adjacent to residential buildings, it is expected that the maximum noise levels will be concentrated within the height range underneath the viaduct. The reflection of noise from the viaduct structure results in the maximum noise level underneath the viaduct being in the middle of the viaduct height. In cases where there are no noise barriers, the noise level on the viaduct initially shows an increasing trend with height. However, with the installation of noise barriers, this upward trend is reversed and the noise level within the acoustic shadow zone gradually decreases as height increases.
  • Within the frequency range of 250 to 4000 Hz, the sound pressure levels exhibit consistent variations with the total sound pressure level. Additionally, due to the lower speeds and lighter weights of urban road vehicles, little low-frequency noise pollution caused by viaduct vibration has been observed.
In our study, the revealed noise distribution pattern updates the understanding of road noise propagation in areas adjacent to elevated complex roads. This unique propagation law helps planners design quieter three-dimensional road traffic systems and adopt more targeted comprehensive noise reduction measures. Meanwhile, the research results provide key references for the improvement direction of future noise prediction models, especially by incorporating the structural parameters of viaducts and the acoustic effects of guardrails. The discussions on nighttime noise fluctuations and low-frequency noise from heavy vehicles are also key points of this paper, through which we hope to inspire more acoustically friendly urban traffic organization strategies. Overall, these insights are particularly applicable to medium and large cities with dense populations, heavy traffic, and urgent needs for three-dimensional road systems, while providing a reference for noise management in other urban environments.

Author Contributions

Conceptualization, G.Y. and Y.W.; methodology, G.Y. and L.H.; formal analysis, G.Y. and L.H.; investigation, G.Y. and Q.L.; data curation, Q.L.; visualization, G.Y. and Q.L.; writing—original draft, G.Y.; writing—review and editing, L.H. and Y.W.; supervision, Y.W.; validation, L.H.; resources, Y.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou grant number No.2023B04J0301 and No.2023B04J0326.

Data Availability Statement

All data used in this study are available from the corresponding author upon request.

Acknowledgments

The authors would like to acknowledge the contribution of Ke Li, Rongjing Zhang, Minghao Xiao, Zixiong Shen and Desi Tu for conducting the in situ measurements and surveys. Many thanks for the support of Academic Specialty Group for Urban Sensing in Chinese Society of Urban Planning.

Conflicts of Interest

Author Lingshan He was employed by the company Guangzhou Urban Planning & Design Survey Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A

The Appendix provides the SPL test results and points locations of each measuring point on the horizontal plane at ground height (Table A1 for Section 3.2) and each measuring point on different floors of the building (Table A2 for Section 3.3).
Table A1. SPL test results and locations of horizontal measuring points at ground height.
Table A1. SPL test results and locations of horizontal measuring points at ground height.
Test AreaPeriodHeight of Measurement Point Above Ground (m)Distance from Road Boundary (m)LAeq (dB(A))
Area #1Period 11.50.267.8
1066.2
2063.8
4062.2
6061.9
Period 21.50.266.9
1065.4
2063
4064.5 (excluded)
6062.2 (excluded)
Period 31.50.266.1
1062.9
2059.5
4055.4
6053.7
Area #2Period 11.50.277.8
576.8
1074.4
2071.9
Period 21.50.276
575.3
1073.4
2071.3
Period 31.50.279.9
578.7
1075.8
2074.3
Table A2. SPL test results and locations of floor-wise measuring points.
Table A2. SPL test results and locations of floor-wise measuring points.
Test AreaPeriodDistance from Road Boundary (m)Height of Measurement Point Above Ground (m)LAeq (dB(A))
Area #1Period 131.5 (1L)69.4
4.1 (2L)68.5
6.7 (3L)67.9
9.3 (4L)70.8
14.5 (6L)71.1
Period 231.5 (1L)69.4
4.1 (2L)68.3
6.7 (3L)67.6
9.3 (4L)69
14.5 (6L)70.4
Period 331.5 (1L)66
4.1 (2L)64.8
6.7 (3L)64.4
9.3 (4L)66.6
14.5 (6L)67.3
Area #2Period 1131.5 (1L)73.3
7.8 (3L)73.9
10.6 (4L)73.2
16.2 (6L)72
21.8 (8L)71.8
Period 2131.5 (1L)73
7.8 (3L)73.8
10.6 (4L)73
16.2 (6L)71.5
21.8 (8L)71
Period 3131.5 (1L)75.3
7.8 (3L)75.7
10.6 (4L)75
16.2 (6L)74
21.8 (8L)73.8

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Buildings 15 02662 g001
Figure 1. Geographical location of Test Area #1 (satellite image source: Baidu maps).
Figure 1. Geographical location of Test Area #1 (satellite image source: Baidu maps).
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Figure 2. Geographical location of Test Area #2 (satellite image source: Baidu maps).
Figure 2. Geographical location of Test Area #2 (satellite image source: Baidu maps).
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Figure 3. Layout diagram of measurement points. (a) shows the layout of Test Area #1; (b) shows the layout of Test Area #2.
Figure 3. Layout diagram of measurement points. (a) shows the layout of Test Area #1; (b) shows the layout of Test Area #2.
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Figure 4. Traffic noise level variations at road boundaries. (a) shows the variations of Test Area #1; (b) shows the variations of Test Area #2.
Figure 4. Traffic noise level variations at road boundaries. (a) shows the variations of Test Area #1; (b) shows the variations of Test Area #2.
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Figure 5. Frequency distribution of traffic noise. (a) shows the result of Test Area #1; (b) shows the result of Test Area #2.
Figure 5. Frequency distribution of traffic noise. (a) shows the result of Test Area #1; (b) shows the result of Test Area #2.
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Figure 6. The field-measured attenuation of traffic noise with different distances.
Figure 6. The field-measured attenuation of traffic noise with different distances.
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Figure 7. Attenuation of SPL in frequency bands of traffic noise with different distances (period 3). (a) shows the result of Test Area #1; (b) shows the result of Test Area #2.
Figure 7. Attenuation of SPL in frequency bands of traffic noise with different distances (period 3). (a) shows the result of Test Area #1; (b) shows the result of Test Area #2.
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Figure 8. Measured vertical distribution of traffic noise at various buildings’ floors. (a) shows the distribution of Building #1; (b) shows the distribution of Building #2.
Figure 8. Measured vertical distribution of traffic noise at various buildings’ floors. (a) shows the distribution of Building #1; (b) shows the distribution of Building #2.
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Figure 9. Frequency distribution of traffic noise at different floors of buildings (period 3). (a) shows the frequency distribution of Building #1; (b) shows the frequency distribution of Building #2.
Figure 9. Frequency distribution of traffic noise at different floors of buildings (period 3). (a) shows the frequency distribution of Building #1; (b) shows the frequency distribution of Building #2.
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Table 1. Noise indicators at road boundaries.
Table 1. Noise indicators at road boundaries.
Test AreaPeriodLAeq (dB(A))Lmax (dB(A))L10 (dB(A))L50 (dB(A))L90 (dB(A))
Area #1Period 167.880.270.267.163.5
Period 266.99268.665.462.7
Period 366.193.268.963.157.7
Area #2Period 177.896.379.276.674.6
Period 27692.977.575.473.3
Period 379.998.682.678.174.7
Table 2. Comparison of the predicted values and actual measurements.
Table 2. Comparison of the predicted values and actual measurements.
Difference (dB(A))Distance (m)
10204060
Minimum−0.7−1.4−1.7−4.1
Maximum1.91.32.42.4
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Yang, G.; He, L.; Wang, Y.; Liu, Q. Impact of Urban Elevated Complex Roads on Acoustic Environment Quality in Adjacent Areas: A Field Measurement Study. Buildings 2025, 15, 2662. https://doi.org/10.3390/buildings15152662

AMA Style

Yang G, He L, Wang Y, Liu Q. Impact of Urban Elevated Complex Roads on Acoustic Environment Quality in Adjacent Areas: A Field Measurement Study. Buildings. 2025; 15(15):2662. https://doi.org/10.3390/buildings15152662

Chicago/Turabian Style

Yang, Guangrui, Lingshan He, Yimin Wang, and Qilin Liu. 2025. "Impact of Urban Elevated Complex Roads on Acoustic Environment Quality in Adjacent Areas: A Field Measurement Study" Buildings 15, no. 15: 2662. https://doi.org/10.3390/buildings15152662

APA Style

Yang, G., He, L., Wang, Y., & Liu, Q. (2025). Impact of Urban Elevated Complex Roads on Acoustic Environment Quality in Adjacent Areas: A Field Measurement Study. Buildings, 15(15), 2662. https://doi.org/10.3390/buildings15152662

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