Assessment of traffic noise levels in urban areas using different soft computing techniques

Tomić, Jelena; Bogojević, Nebojša; Pljakić, M.; Šumarac-Pavlović, D.

doi:10.1121/1.4964786

Cited by 25 publications

(16 citation statements)

References 8 publications

(8 reference statements)

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“…The shortcoming of this study is that the LSTM network structure used is relatively simple, and in the future, a more in-depth, broader and more powerful optimized LSTM model can be designed to improve accuracy. At the same time, the LSTM does not provide insight into the physical meaning of their parameters [62], in addition to time, more variables should be considered.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…The CRTN model also contributed significantly to the development of the relevant International Standard in the mid 1990's [29]. However, as any other mathematical model which has been developed by statistical analysis of experimental data, the CRTN method has been strongly influenced by the characteristics of the measurement location (traffic and site variables) in which this (model) was originated [30]. Therefore it is important to evaluate the prediction accuracy of the CRTN model, whenever this is called for use in regions of distinct local characteristics.…”

Section: Introductionmentioning

confidence: 99%

Measurements and prediction of road traffic noise along high-rise building façades in Athens

et al. 2020

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Constant exposure to traffic noise pollution can have significant impact on human health and well being. Occupants of high-rise buildings along noisy traffic arteries are severely affected. In an attempt to contribute to noise protection design of prospective high-rise buildings, traffic noise measurements and prediction using the CRTN (calculation of road traffic noise) model, were made along the façade of a high-rise building in central Athens. The aim was to test the accuracy of this model in predicting the vertical distribution (mapping) of traffic noise along such building façades, under the local urban characteristics of the Mediterranean capital. The predicted and measured noise levels were found to be highly coherent with each other, and their vertical distribution pattern, by and large, confirmed findings from earlier studies. Nevertheless, the predicted values had a tendency of underestimation, with a mean difference −2.2 dB(A) with reference to measured values. It is considered that this underestimation is associated mainly with a newly proposed feature of urban morphology, namely (local) geo-morphology. By and large, it can be inferred that the CRTN model is a useful tool, suitable for the prediction of traffic noise along high-rise building façades during their planning and design stage. The results represent a further step towards more general application of this model, as well as a contribution to the use of this model considering a wider number of urban features.

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“…Almost all these models are designed by using experimental samples; consequently, these models are highly influenced by the traffic flow condition and the measurement style and the geographic locations [14]. The main drawback of these models is that they can not be generalized because of the local environment like vehicle model and type and the weather [15,16]. Ref.…”

Section: Introductionmentioning

confidence: 99%

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

et al. 2019

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Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.

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Assessment of traffic noise levels in urban areas using different soft computing techniques

Cited by 25 publications

References 8 publications

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

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

Measurements and prediction of road traffic noise along high-rise building façades in Athens

Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

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