Enhanced Estimation of Traffic Noise Levels Using Minute-Level Traffic Flow Data through Convolutional Neural Network

Wencheng Yu, Ji-Cheng Jang, Yun Zhu (), Jianxin Peng, Wenwei Yang and Kunjie Li
Additional contact information
Wencheng Yu: School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
Ji-Cheng Jang: School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
Yun Zhu: School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
Jianxin Peng: School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510640, China
Wenwei Yang: Cloud & Information (Guangdong) Eco-Environment Science and Technology Co., Ltd., Foshan 528000, China
Kunjie Li: School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China

Sustainability, 2024, vol. 16, issue 14, 1-16

Abstract: The advent of high-resolution minute-level traffic flow data from video surveillance on roads has opened up new opportunities for enhancing the estimation of traffic noise levels. In this study, we propose an innovative method that utilizes time series traffic flow data (TSTFD) to estimate traffic noise levels using a deep learning Convolutional Neural Network (CNN). Unlike traditional traffic flow data, TSTFD offer a unique structure and composition suitable for multidimensional data analysis. Our method was evaluated in a pilot study conducted in Foshan City, China, utilizing traffic flow information obtained from roadside video surveillance systems. Our results indicated that the CNN-based model surpassed traditional data-driven statistical models in estimating traffic noise levels, achieving a reduction in mean squared error (MSE) by 10.16%, mean absolute error (MAE) by 4.48%, and an improvement in the coefficient of determination (R²) by 1.73%. The model demonstrated robust generalization capabilities throughout the test period, exhibiting mean errors ranging from 0.790 to 1.007 dBA. However, the model’s applicability is constrained by the acoustic propagation environment, demonstrating effectiveness on roads with similar surroundings while showing limited applicability to those with different surroundings. Overall, this method is cost-effective and offers enhanced accuracy for the estimation of traffic noise level.

Keywords: traffic noise; noise modeling; time series traffic flow; CNN; traffic flow feature (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 references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/14/6088/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/14/6088/ (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:14:p:6088-:d:1436549

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 ().