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

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.

Traffic Noise Modeling under Mixed Traffic Condition in Mid-Sized Indian City: A Linear Regression and Neural Network-Based Approach

“…R 2 values in the range of 0.81 to 0.85 were attained by studies in Indian cities like Delhi, Patiala (Garg et al, 2015;Kumar et al, 2014;Singh et al, 2016), Chongqing city, China (Chen et al, 2020), and New Klang Valley, Malaysia (Ahmed et al, 2021). However, studies in Surat City, India, and Granada, Spain, showed a slightly lower R 2 ranging from 0.75-0.76 (Genaro et al, 2010;Ranpise et al, 2021). A novel approach employed an Emotional Neural Network in Nicosia, North Cyprus, resulting in an R 2 of 0.81 (Nourani et al, 2020).…”

Calculation of Road Traffic Noise, Development of Data, and Spatial Interpolations for Traffic Noise Visualization in Three-dimensional Space

“…Therefore, several amendments to existing noise models are required for traffic noise calculations [14] and reviewing the standard road traffic noise model is vital to road traffic noise mapping. Sounds from engine noise, tire and road friction, road surface type, and rolling noise are the main contributors to the generation of traffic noise [15]. Furthermore, road traffic, human qualities [16], transportation networks, and environmental conditions all lead to increased traffic noise pollution [17].…”

Predicting traffic induced noise using artificial neural network and multiple linear regression approach

“…They took into account the normality and non-normality of participation for various noise parameters, such as noise intensity, exposure period, and the impacted age group of people in a specific location, and graphical depictions were created for the model's overall rationale [28]. Ranpise et al ( 2021) measured ambient noise levels along key arterial roads in Surat, compared them to mandated criteria, and developed a noise prediction model for arterial roads based on an ANN with a feed-forward back propagation method for training [29]. Machine learning (ML) modelling approaches were utilised by Ali et al (2019) to accurately estimate roadway traffic noise.…”