Probabilistic Prediction Model for Expressway Traffic Noise Based on Short-Term Monitoring Data

Feng Li (), Haibo Wang, Canyi Du, Ziqin Lan (), Feifei Yu and Ying Rong
Additional contact information
Haibo Wang: School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Canyi Du: School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, China
Ziqin Lan: School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, China
Feifei Yu: School of Mechatronic Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, China
Ying Rong: School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510450, China

Sustainability, 2024, vol. 16, issue 16, 1-17

Abstract: Seeking a straightforward and efficient method to predict expressway traffic noise, this study selected three expressway segments in Guangdong Province, China and conducted noise monitoring at ten different sites along these expressways. Data analysis revealed that the mean sound levels and standard deviations were significantly positively and negatively correlated with traffic volume, respectively, and the frequency distribution of sound levels closely resembled a normal distribution. A probability prediction model for expressway traffic noise, based on a normal distribution, has been constructed utilizing these characteristics. The mean and standard deviation of the model were determined using a linear regression method, and the relationship between the mean, standard deviation, and various noise evaluation indices was derived from the characteristics of the normal distribution. The proposed model enables the direct prediction of the statistical frequency distribution of sound levels and various noise evaluation indices. Despite using only two five-minute segments of monitoring data for training, the model’s average prediction error for L eq , L 10 , L 50 , and L 90 was only 1.06, 1.07, 1.04, and 1.32 dB(A). With increased sample data for modeling, the model’s predictive accuracy notably improved. This study provides a highly effective predictive tool for assessing traffic noise for residents near expressways.

Keywords: traffic noise; expressway; short-term monitoring data; probabilistic prediction model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/16/6841/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/16/6841/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:16:p:6841-:d:1453262

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().