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Influence of Traffic Noise on Residential Environment
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Influence of Traffic Noise on Residential Environment

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2222

Special Issue Editors

Dr. Haibo Wang

E-Mail Website
Guest Editor
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: traffic noise; transportation environment; urban and regional planning; intelligent transportation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ming Cai

E-Mail Website
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: transportation environment; transportation noise; intelligent transportation; traffic big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Various modes of transportation, including highway, railway, waterway, and air, bring about noise pollution which cannot be ignored. This pollution causes both psychological and physiological effects to human health and also worsens the residential environment. Methods to reasonably evaluate and control traffic noise and reduce its influence on residents are directly related to human quality of life. Recently, there has been a number of studies on the impact of traffic noise on human settlements in terms of policies, theories, and methods. However, due to the complex traffic network, the application of new technology in planning and building construction, and the emphasis on human factors, understanding and mitigation of the impact of traffic noise on residential environment are facing new opportunities and challenges.

We are pleased to invite you to submit a paper to the Special Issue “Influence of Traffic Noise on Residential Environment” of the International Journal of Environmental Research and Public Health (IJERPH). This Special Issue seeks research papers on traffic noise influence of indoor/outdoor residential environments, which include various transportation modes and auditory/non-auditory impacts on human health and the environment.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: measurement and prediction of traffic noise, assessment of indoor and outdoor acoustics environment, urban traffic environment planning, noise management and control technology, noise mapping and practical research, traffic noise exposure, health of residential public health, etc. It is expected that this Special Issue will provide a deeper understanding of the effect of noise pollution on human health through high-quality research.

You may choose our Joint Special Issue in International Journal of Environmental Research and Public Health.

Dr. Haibo Wang
Prof. Dr. Ming Cai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • traffic noise
  • indoor/outdoor acoustics environment
  • traffic noise exposure
  • soundscape
  • urban noise planning
  • noise prediction and assessment
  • psychological and physiological effects on health

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Published Papers (2 papers)

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Research

17 pages, 6333 KiB  
Article
Probabilistic Prediction Model for Expressway Traffic Noise Based on Short-Term Monitoring Data
by Feng Li, Haibo Wang, Canyi Du, Ziqin Lan, Feifei Yu and Ying Rong
Sustainability 2024, 16(16), 6841; https://doi.org/10.3390/su16166841 - 9 Aug 2024
Viewed by 831
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 [...] Read more.
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 Leq, L10, L50, and L90 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. Full article
(This article belongs to the Special Issue Influence of Traffic Noise on Residential Environment)
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16 pages, 7364 KiB  
Article
Enhanced Estimation of Traffic Noise Levels Using Minute-Level Traffic Flow Data through Convolutional Neural Network
by Wencheng Yu, Ji-Cheng Jang, Yun Zhu, Jianxin Peng, Wenwei Yang and Kunjie Li
Sustainability 2024, 16(14), 6088; https://doi.org/10.3390/su16146088 - 17 Jul 2024
Viewed by 1033
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Influence of Traffic Noise on Residential Environment)
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