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Special Issue "Influence of Traffic Noise on Residential Environment"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: 1 January 2024 | Viewed by 807

Special Issue Editors

School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: traffic noise; transportation environment; urban and regional planning; intelligent transportation
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: transportation environment; transportation noise; intelligent transportation; traffic big data

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.

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. International Journal of Environmental Research and Public Health 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 2500 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.


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

Published Papers (1 paper)

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Deep Learning-Based Road Traffic Noise Annoyance Assessment
Int. J. Environ. Res. Public Health 2023, 20(6), 5199; https://doi.org/10.3390/ijerph20065199 - 15 Mar 2023
Viewed by 432
With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has [...] Read more.
With the development of urban road traffic, road noise pollution is becoming a public concern. Controlling and reducing the harm caused by traffic noise pollution have been the hot spots of traffic noise management research. The subjective annoyance level of traffic noise has become one of the most important measurements for evaluating road traffic pollution. There are subjective experimental methods and objective prediction methods to assess the annoyance level of traffic noise: the subjective experimental method usually uses social surveys or listening experiments in laboratories to directly assess the subjective annoyance level, which is highly reliable, but often requires a lot of time and effort. The objective method extracts acoustic features and predicts the annoyance level through model mapping. Combining the above two methods, this paper proposes a deep learning model-based objective annoyance evaluation method, which directly constructs the mapping between the noise and annoyance level based on the listening experimental results and realizes the rapid evaluation of the noise annoyance level. The experimental results show that this method has reduced the mean absolute error by 30% more than the regression algorithm and neural network, while its performance is insufficient in the annoyance interval where samples are lacking. To solve this problem, the algorithm adopts transfer learning to further improve the robustness with a 30% mean absolute error reduction and a 5% improvement in the correlation coefficient between the true results and predicted results. Although the model trained on college students’ data has some limitations, it is still a useful attempt to apply deep learning to noise assessment. Full article
(This article belongs to the Special Issue Influence of Traffic Noise on Residential Environment)
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