Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

被引:1
作者
Elvas, Luis B. [1 ,2 ,3 ]
Nunes, Miguel [1 ]
Ferreira, Joao C. [1 ,2 ,3 ]
Francisco, Bruno [1 ]
Afonso, Jose A. [4 ,5 ]
机构
[1] Inst Univ Lisboa ISCTE IUL, ISTAR, P-1649026 Lisbon, Portugal
[2] Inov Inesc Inovacao Inst Novas Tecnol, P-1000029 Lisbon, Portugal
[3] Molde Univ Coll, Dept Logist, N-6410 Molde, Norway
[4] Univ Minho, CMEMS UMinho, P-4800058 Guimaraes, Portugal
[5] Univ Minho, LABBELS Associate Lab, Guimaraes, Portugal
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 01期
关键词
mobile phone sensing; machine learning; noise patterns; urban environments; clustering algorithms; ELECTRIC VEHICLE NOISE; SMART CITIES; PREDICTION; ZONE;
D O I
10.3390/app14010362
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development.
引用
收藏
页数:20
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