Urban road traffic noise spatiotemporal distribution mapping using multisource data |
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| Institution: | 1. School of Intelligent Systems Engineering, Sun Yat-Sen University, Shenzhen 518107, China;2. Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510006, China;3. Environmental Monitoring Station of Chancheng District, Foshan 528000, China;1. Department of Geography, Malda Women’s Collage, Malda, India;2. Department of Geography, University of Gour Banga, Malda, India;1. School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China;2. Guangdong Provincial Key Laboratory of Intelligent Transportation System, Guangzhou, China;3. School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, China;1. GTM – Grup de recerca en Tecnologies Mèdia, La Salle – URL, c/Quatre Camins, 30, 08022 Barcelona, Spain;2. Department of Earth and Environmental Sciences (DISAT), Universitá degli Studi di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy;1. Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy;2. Department of Physics, University of Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, Italy;1. Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan;2. Department of Pharmacy, College of Pharmacy, China Medical University, Taichung, Taiwan |
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| Abstract: | Many residents are disturbed by road traffic noise which needs to be controlled and managed. The noise map is a helpful and important tool for noise management and acoustical planning in urban areas. However, the static noise map is not sufficient for evaluating noise annoyance at different temporal periods. It is necessary to develop the dynamic noise map or the noise spatiotemporal distribution. In this study, a method about urban road traffic noise spatiotemporal distribution mapping is proposed to obtain the representative road traffic noise maps of different periods. This method relies on the proposed noise spatiotemporal distribution model with two time-dependent variables - traffic density and traffic speed, and the spatiotemporal characteristics derived from multisource data. There are three steps in the method. First, the urban road traffic noise spatiotemporal distribution model is derived from the law of sound propagation. Then, the temporal characteristics are extracted from traffic flow detecting data and E-map road segment speed data by the outlier detection analysis. Finally, the noise distributions corresponding to different periods are calculated by an efficient algorithm which can save 90% above of the computing time. Moreover, a validation experiment was conducted to evaluate the accuracy of the proposed method. There is only 2.26-dBA] mean absolute error that is within an acceptable range, which shows that the method is effective. |
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| Keywords: | Road traffic noise Spatiotemporal distribution Noise mapping Multisource data |
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