Vehicular Traffic Noise Modelling of Urban Area- A Contouring & Artificial Neural Network-Based Approach

Abstract Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causing health risks among the urban populations. In this study we have explored noise descriptors (L10, L90, Ldn, LNI, TNI, NC), contour plotting and finds the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, Speed, traffic flow, road gradient, pavement, road side carriageway distance factors taken as input parameter, whereas LAeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59% 2-wheelers and different vehicle specifications with varying speeds also effects driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicates high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 & .029 in training and 0.458 & 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ±0.6 dB(A) and the R2 linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

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Application of Artificial Neural Network to Prediction of Traffic Noise Levels in the City of Niš, Serbia

Springer Proceedings in Physics - Acoustics and Vibration of Mechanical Structures—AVMS-2017 ◽

10.1007/978-3-319-69823-6_11 ◽

2017 ◽

pp. 91-98 ◽

Cited By ~ 1

Author(s):

Jelena Tomić ◽

Nebojša Bogojević ◽

Zlatan Šoškić

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Noise ◽

Noise Levels ◽

Artificial Neural ◽

The City

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Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

Electronics ◽

10.3390/electronics8020163 ◽

2019 ◽

Vol 8 (2) ◽

pp. 163 ◽

Cited By ~ 6

Author(s):

Emir Turajlic ◽

Alen Begović ◽

Namir Škaljo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Noise Level ◽

Low Noise ◽

Singular Value ◽

Fine Tuning ◽

Noise Levels ◽

Noise Level Estimation ◽

Artificial Neural ◽

Block Based

The blind additive white Gaussian noise level estimation is an important and a challenging area of digital image processing with numerous applications including image denoising and image segmentation. In this paper, a novel block-based noise level estimation algorithm is proposed. The algorithm relies on the artificial neural network to perform a complex image patch analysis in the singular value decomposition (SVD) domain and to evaluate noise level estimates. The algorithm exhibits the capacity to adjust the effective singular value tail length with respect to the observed noise levels. The results of comparative analysis show that the proposed ANN-based algorithm outperforms the alternative single stage block-based noise level estimating algorithm in the SVD domain in terms of mean square error (MSE) and average error for all considered choices of block size. The most significant improvements in MSE levels are obtained at low noise levels. For some test images, such as “Car” and “Girlface”, at σ = 1 , these improvements can be as high as 99% and 98.5%, respectively. In addition, the proposed algorithm eliminates the error-prone manual parameter fine-tuning and automates the entire noise level estimation process.

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SHORT TERM TRAFFIC FLOW PREDICTION IN HETEROGENEOUS CONDITION USING ARTIFICIAL NEURAL NETWORK

Transport ◽

10.3846/16484142.2013.818057 ◽

2013 ◽

Vol 30 (4) ◽

pp. 397-405 ◽

Cited By ~ 37

Author(s):

Kranti Kumar ◽

Manoranjan Parida ◽

Vinod Kumar Katiyar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Flow ◽

Traffic Congestion ◽

Traffic Volume ◽

Flow Data ◽

Highway Traffic ◽

Short Term ◽

Artificial Neural ◽

Input Variables

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

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Development of traffic noise prediction model for major arterial roads of tier-II city of India (Surat) using artificial neural network

Noise Mapping ◽

10.1515/noise-2021-0013 ◽

2021 ◽

Vol 8 (1) ◽

pp. 172-184

Author(s):

Ramesh B. Ranpise ◽

B. N. Tandel ◽

Vivek A. Singh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Prediction Models ◽

Traffic Noise ◽

Road Traffic Noise ◽

Noise Prediction ◽

Noise Levels ◽

Street Traffic ◽

Artificial Neural

Abstract In the issue of expanding noise levels the world over, road traffic noise is main contributor. The investigation of street traffic noise in urban communities is a significant issue. Ample opportunity has already passed to understand the significance of noise appraisal through prediction models with the goal that assurance against street traffic noise can be actualized. Noise predictions models are utilized in an increasing range of decision-making applications. This study’s main objective is to assess ambient noise levels at major arterial roads of Surat city, compare these with prescribed standards, and develop a noise prediction model for arterial roads using an Artificial Neural Network. The feed-forward back propagation method has been used to train the model. Models have been developed using the data of three roads separately, and one final model has also been developed using the data of all three roads. Among the prediction in three arterial roads, the predicted output result from the model of Adajan-Rander showed a better correlation with a mean squared error (MSE) of 0.789 and R2 value of 0.707. But with the combined model, there is a slight deterioration in mean squared value (MSE) 1.550, with R2 not getting changed much significantly, i.e., 0.755. However, the combined model’s prediction can be adopted due to the variety of data used in its training.

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Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

Communications - Scientific letters of the University of Zilina ◽

10.26552/com.c.2022.2.e74-e86 ◽

2022 ◽

Vol 24 (2) ◽

pp. E74-E86

Author(s):

Isaac Oyeyemi Olayode ◽

Alessandro Severino ◽

Tiziana Campisi ◽

Lagouge Kwanda Tartibu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Traffic Flow ◽

Traffic Control ◽

Traffic Congestion ◽

Autonomous Vehicles ◽

Time Of Day ◽

Vehicle Speed ◽

Road Transportation ◽

Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

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