TY - JOUR
T1 - Modelling of traffic noise in the vicinity of urban road intersections
AU - Yadav, Adarsh
AU - Mandhani, Jyoti
AU - Parida, Manoranjan
AU - Kumar, Brind
N1 - Funding Information:
The research is supported by the IMPRINT Project under a grant from the Ministry of Housing and Urban Affairs (MoHUA) and the Ministry of Education (MoE), Government of India. Also, the authors acknowledge the Ministry of Education (MoE) for awarding the Ph.D. scholarship to one of the co-authors, Mr. Adarsh Yadav.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/11
Y1 - 2022/11
N2 - Traffic noise is continuously rising alongside roadways, especially at intersections, due to rapid urbanization, eventually affecting acoustical climate and quality of life. This present study develops a traffic noise model for intersections with minimal evidence of interrelationships among influential traffic noise factors. An integrated Bayesian networks and Partial least squares structural equation modelling approach has been employed on 342-hour field measurement data collected from nineteen intersections in Kanpur, India. The integrated approach developed the traffic noise prediction model with 62.4% explanatory power and identified direct and indirect effects of five influential factors on traffic noise. For instance, Traffic flow attributes, i.e., traffic volume and honking, are the most crucial ones to degrade acoustical climate at intersections. Besides, Built environment and Climate conditions induce only indirect effects on traffic noise. Thus, this study provides a useful basis for planners to understand traffic noise relationships and deploy noise mitigation strategies accordingly.
AB - Traffic noise is continuously rising alongside roadways, especially at intersections, due to rapid urbanization, eventually affecting acoustical climate and quality of life. This present study develops a traffic noise model for intersections with minimal evidence of interrelationships among influential traffic noise factors. An integrated Bayesian networks and Partial least squares structural equation modelling approach has been employed on 342-hour field measurement data collected from nineteen intersections in Kanpur, India. The integrated approach developed the traffic noise prediction model with 62.4% explanatory power and identified direct and indirect effects of five influential factors on traffic noise. For instance, Traffic flow attributes, i.e., traffic volume and honking, are the most crucial ones to degrade acoustical climate at intersections. Besides, Built environment and Climate conditions induce only indirect effects on traffic noise. Thus, this study provides a useful basis for planners to understand traffic noise relationships and deploy noise mitigation strategies accordingly.
KW - Bayesian networks
KW - Influencing factors
KW - Partial least squares structural equation modelling (PLS-SEM)
KW - Traffic noise
KW - Urban intersections
UR - http://www.scopus.com/inward/record.url?scp=85140030262&partnerID=8YFLogxK
U2 - 10.1016/j.trd.2022.103474
DO - 10.1016/j.trd.2022.103474
M3 - Article
SN - 1361-9209
VL - 112
JO - Transportation Research Part D: Transport and Environment
JF - Transportation Research Part D: Transport and Environment
M1 - 103474
ER -