Evaluation of road traffic noise exposure based on high-resolution population distribution and grid-level noise data

Cai, Ming; Lan, Ziqin; Zhang, Zhiwei; Wang, Haibo

doi:10.1016/j.buildenv.2018.08.037

Cited by 60 publications

(24 citation statements)

References 40 publications

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“…Based on monitoring or simulation data, noise distribution rules and pollution exposure with different dimensions are analyzed in recent decades. Such as: Cai et al [35] suggested an approach considering high-resolution population and grid noise level to evaluate noise exposure in large urban area; Focused on building interiors, Funkhouse [36] presented a 3D model for architectural acoustics method; Hornikx et al [37] predicted sound propagation in urban canyons and courtyards; Licitra et al [38] presented comparative analysis of methods to estimate urban noise exposure of inhabitants, respectively. Additionally, the issue of noise distribution around urban buildings has received considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

Wang

Cai

Cui

2019

IJERPH

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In order to realize the simulation and evaluation of road traffic noise among urban buildings, a spatial subdivision-based beam-tracing method is proposed in this study. First, the road traffic source is divided into sets of point sources and described with the help of vehicle emission model. Next, for each pair of source and receiver, spatial subdivision-based beam-tracing method is used in noise paths generation. At last, noise distribution can be got by noise calculation of all receivers considering the complex transmission among urban buildings. A measurement experiment with a point source is carried out to validate the accuracy of the method; the 0.8 m height and 2.5-m height average errors are about 0.9 dB and 1.2 dB, respectively. Moreover, traffic noise analysis under different building layouts and heights are presented by case applications and conclusions can be reached: (1) Different patterns result in different noise distributions and patterns designed as self-protective can lead to an obvious noise abatement for rear buildings. Noise differences between the front and rear buildings are about 7–12 dB with different patterns. (2) Noise value might not show a linear variation along with the height as shielding of different layers is various in reality.

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Section: Introductionmentioning

confidence: 99%

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

Wang

Cai

Cui

2019

IJERPH

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“…Usually, noise levels are mapped using a geographic information system (GIS) to quantify and visualize the effects of noise based on the measurement of noise levels at various locations [11]. Furthermore, Tansatcha et al studied the factors affecting the generation of traffic noise such as traffic volume and combinations, number of lanes, shoulder width, and spot speeds.…”

Section: Introductionmentioning

confidence: 99%

Modelling of Traffic Noise Along Urban Corridor: A Case Study of Amman

Younes

Suleiman²,

Abushammala

et al. 2021

Civil and Environmental Engineering

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Noise became one of the main environmental indicators of the quality of urban life. The aim of this study was to develop a traffic noise model for an arterial road in Amman, the capital of Jordan, which was being subjected to a persistent increase in traffic and its related issues. The characteristics of the traffic and its relevant noise were analysed to develop a noise prediction model using an adaptive neuro-fuzzy inference system (ANFIS). A geographic information system (GIS) was then implemented to model the current noise along the arterial road. The results of ANFIS model showed that traffic delays and the percentage of heavy vehicles were the main causes of traffic noise generation within the urban area. However, the developed ANFIS model can simulate traffic noise with a relatively low root mean square error. Furthermore, this study will help to improve the perception about traffic noise and its development along arterial roads.

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“…An et al [31] used the location-specific license plates recognised by AVI not only to collect high real-time information from vehicles but also to estimate a lane-based traffic arrival pattern. Given its rich spatial-temporal information, AVI data has been widely used to provide more advanced intelligent transportation services [32][33][34]. But a gap is observed between the wide deployment of AVI detectors and the potential applications in the identification, internal cause analysis, and alleviation of the congestion around the service centers.…”

Section: Introductionmentioning

confidence: 99%

Relieving the Congestion around a School via Automatic Vehicle Identification Technology-Based Traffic Measures

Sun³

et al. 2021

Journal of Advanced Transportation

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Recently, many parents drive their children to and from schools, leading to serious road congestion around the school gate. The school-related congestion is a special type of congestion caused by periodic impulsive aggregation of specific travellers for certain events. In this study, the individual long short-term traffic behaviours were reconstructed based on automatic vehicle identification (AVI) technologies. The cause and countermeasure of congestion around the service centers were identified through the individual behavioural properties. The vehicles that were primarily responsible for periodic impulsive aggregation congestion (PIAC) around the school gate were precisely targeted via a proposed vehicle grading clustering framework. The road management objectives were updated in the AVI data environment and it was found that only 3%–5% of the total number of vehicles passing by the school gate require specific management such as traffic enforcement activities. A series of traffic measures were formulated based on the results of vehicle grading clustering and achieved positive effects in a periodic impulsive aggregation area. It is an effective way to solve the PIAC by formulating management with different activity levels and resolutions for specific travellers. The methodologies and experience presented in this study may provide a useful tool for relieving such special type of congestion around other service centers faced with similar scenarios.

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Evaluation of road traffic noise exposure based on high-resolution population distribution and grid-level noise data

Cited by 60 publications

References 40 publications

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

Modelling of Traffic Noise Along Urban Corridor: A Case Study of Amman

Relieving the Congestion around a School via Automatic Vehicle Identification Technology-Based Traffic Measures

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