Model for Determining Noise Level Depending on Traffic Volume at Intersections
2022
Autori
Ruskić, NenadMirović, Valentina
Marić, Milovan
Pezo, Lato
Loncar, Biljana
Nicetin, Milica
Ćurčić, Ljiljana
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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.
Ključne reči:
traffic volume / random forest / noise / modeling / artificial neural network modelIzvor:
Sustainability, 2022, 14, 19Izdavač:
- MDPI, BASEL
Finansiranje / projekti:
- Modeli integracije transportnog sistema (RS-MESTD-Technological Development (TD or TR)-36024)
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200034 (Univerzitet u Beogradu, Institut 'Mihajlo Pupin') (RS-MESTD-inst-2020-200034)
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200051 (Institut za opštu i fizičku hemiju, Beograd) (RS-MESTD-inst-2020-200051)
- Ministarstvo nauke, tehnološkog razvoja i inovacija Republike Srbije, institucionalno finansiranje - 200134 (Univerzitet u Novom Sadu, Tehnološki fakultet) (RS-MESTD-inst-2020-200134)
Institucija/grupa
Fakultet za studije bezbednostiTY - JOUR AU - Ruskić, Nenad AU - Mirović, Valentina AU - Marić, Milovan AU - Pezo, Lato AU - Loncar, Biljana AU - Nicetin, Milica AU - Ćurčić, Ljiljana PY - 2022 UR - https://redun.educons.edu.rs/handle/123456789/448 AB - 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. PB - MDPI, BASEL T2 - Sustainability T1 - Model for Determining Noise Level Depending on Traffic Volume at Intersections IS - 19 VL - 14 DO - 10.3390/su141912443 UR - conv_1136 ER -
@article{ author = "Ruskić, Nenad and Mirović, Valentina and Marić, Milovan and Pezo, Lato and Loncar, Biljana and Nicetin, Milica and Ćurčić, Ljiljana", year = "2022", 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.", publisher = "MDPI, BASEL", journal = "Sustainability", title = "Model for Determining Noise Level Depending on Traffic Volume at Intersections", number = "19", volume = "14", doi = "10.3390/su141912443", url = "conv_1136" }
Ruskić, N., Mirović, V., Marić, M., Pezo, L., Loncar, B., Nicetin, M.,& Ćurčić, L.. (2022). Model for Determining Noise Level Depending on Traffic Volume at Intersections. in Sustainability MDPI, BASEL., 14(19). https://doi.org/10.3390/su141912443 conv_1136
Ruskić N, Mirović V, Marić M, Pezo L, Loncar B, Nicetin M, Ćurčić L. Model for Determining Noise Level Depending on Traffic Volume at Intersections. in Sustainability. 2022;14(19). doi:10.3390/su141912443 conv_1136 .
Ruskić, Nenad, Mirović, Valentina, Marić, Milovan, Pezo, Lato, Loncar, Biljana, Nicetin, Milica, Ćurčić, Ljiljana, "Model for Determining Noise Level Depending on Traffic Volume at Intersections" in Sustainability, 14, no. 19 (2022), https://doi.org/10.3390/su141912443 ., conv_1136 .