A modified 3D algorithm for road traffic noise attenuation calculations in large urban areas

Wang, Haibo; Cai, Ming; Yao, Yifan

doi:10.1016/j.jenvman.2017.03.039

Cited by 22 publications

(8 citation statements)

References 32 publications

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“…The current studies to calculate simulated noise focus primarily on the empirical method and the geometric acoustic method. The empirical methods such as ISO model [23] and its developing model [24,25], taking building density and the effects of front buildings into consideration, resulting in relatively large errors. Validated with reasonable accuracy and efficiency [26,27], the geometric acoustic method has been applied in traffic noise simulation, which consists of beam tracing method (BTM) [28,29], ray tracing method (RTM) [30,31] and image source method (ISM) [32].…”

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

Self Cite

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“…861 points were set in which the values of the noise level were recorded for three seasons (summer, winter, spring-autumn). The Spatio-temporal studies of the noise level in the city are focused on the analysis of noise impact from the main noise source -vehicles [1], therefore, for further data comparison, all measurements were carried out in the middle of the working week (Wednesday, Thursday) during the evening rush hour from 18.30 to 20.00, corresponding to the highest intensity of traffic flows in all studied seasons of the year.…”

Section: Materials and Research Methodsmentioning

confidence: 99%

“…The level of urbanization in the world is increasing, leading to an increase in the noise level exposure for the population. The main source of noise in cities is road transport, which significantly increases the noise impact on residential areas located near highways [1]. Currently, the study of the spread of noise is becoming more and more relevant in connection with the implementation of measures for the renovation of dilapidated housing in many large urban agglomerations [2].…”

Section: Introductionmentioning

confidence: 99%

Specific features of noise spreading from motor transport in the urban environment

Margolina

Veselov

2020

MATEC Web Conf.

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The work is devoted to the study of the features of Spatio-temporal changes in the noise level from vehicles and is based on field measurements carried out in the autumn-spring, summer and winter seasons. The measurements were carried out on the territory of the administrative district of Moscow at 861 points during the evening rush hour. For the analysis of space-time series, a cartographic research method was used using the QuantumGIS and ArcGis programs. The series of maps created in this work demonstrates the dependence of the noise level in the urban area on the traffic load, the type of urban planning, and seasonality (the presence of snow cover and the vegetation state of plant complexes). Zoning of the territory by the noise influence makes it possible to identify zones with a critical and high noise level (above the PEL standard), as well as areas with an insignificant and medium noise level (below the PEL).

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“…IoT must overcome some challenges to extract new insights from data [49]. In previous studies [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], environmental noise prediction mainly focused on the spatial propagation of noise, and there were few studies focused on the variation of noise in short-term. The noise data set used in this study is in seconds, which is more random.…”

Section: Proposed Lstm Model Frameworkmentioning

confidence: 99%

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

2020

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Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). The proposed model outperforms the other three existing classic methods. The time interval of the data set has a close connection with the performance of all models. The results revealed that the LSTM network could reflect changes in noise levels within one day and has good prediction accuracy. Impacts of monitoring point location on prediction results and recommendations for environmental noise management were also discussed in this paper.

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A modified 3D algorithm for road traffic noise attenuation calculations in large urban areas

Cited by 22 publications

References 32 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

Specific features of noise spreading from motor transport in the urban environment

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

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