Model for Determining Noise Level Depending on Traffic Volume at Intersections

Nenad Ruškić, Valentina Mirović, Milovan Marić, Lato Pezo, Biljana Lončar (), Milica Nićetin and Ljiljana Ćurčić
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Nenad Ruškić: Department of Traffic Engineering, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Valentina Mirović: Department of Traffic Engineering, Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
Milovan Marić: Institute Mihajlo Pupin, University of Belgrade, Volgina 15, 11060 Beograd, Serbia
Lato Pezo: Institute of General and Physical Chemistry, University of Belgrade, Studentski Trg 12, 11000 Belgrade, Serbia
Biljana Lončar: Faculty of Technology Novi Sad, University of Novi Sad, Bul. Cara Lazara 1, 21000 Novi Sad, Serbia
Milica Nićetin: Faculty of Technology Novi Sad, University of Novi Sad, Bul. Cara Lazara 1, 21000 Novi Sad, Serbia
Ljiljana Ćurčić: Faculty of Environmental Protection, Educons University, Vojvode Putnika 85–87, 21208 Sremska Kamenica, Serbia

Sustainability, 2022, vol. 14, issue 19, 1-17

Abstract: The negative external effects caused by traffic growth have been recognized as the main factors that degrade city quality of life. Therefore, research around the world is being conducted to understand the impact of traffic better and find adequate measures to reduce the negative impact of traffic growth. The central part of this research consists of mathematical models for simulating the negative consequences of congestion and noise pollution. Four non-linear models for determining noise levels as a function of traffic flow parameters (intensity and structure) in the urban environment were developed. The non-linear models, including two artificial neural networks and two random forest models, were developed according to the experimental measurements in Novi Sad, Serbia, in 2019. These non-linear models showed high anticipation accuracy of the equivalent continuous sound level (Laeq), with R 2 values of 0.697, 0.703, 0.959 and 0.882, respectively. According to the developed ANN models, global sensitivity analysis was performed, according to which the number of buses at crossings was the most positively signed influential parameter in Laeq evaluation, while the lowest Laeq value was reached during nighttime. The locations occupied by frequent traffic such as Futoska and Temerinska positively influenced the Laeq value.

Keywords: noise; traffic volume; modeling; artificial neural network model; random forest (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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