Study of temporal correlations in the urban noise monitoring network of milan, italy

被引:0
|
作者
Benocci R. [1 ]
Roman H.E. [2 ]
Confalonieri C. [1 ]
Zambon G. [1 ]
机构
[1] Department of Environmantal Sciences, University of Milano Bicocca, Milano
[2] Department of Physics, University of Milano Bicocca, Milano
来源
International Journal of Circuits, Systems and Signal Processing | 2020年 / 14卷
关键词
DYNAMAP; Dynamic noise map; Noise prediction; Temporal correlations;
D O I
10.46300/9106.2020.14.69
中图分类号
学科分类号
摘要
The European Life project, called DYNAMAP, has been devoted to provide a real image of the noise generated by vehicular traffic in urban and suburban areas, developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. In the urban area of Milan, the system has been implemented over the pilot area named Area 9. Traffic noise data, collected by the monitoring stations, each one representative of a number of roads with similar characteristics (e.g. daily traffic flow), are used to build-up a “real time” noise map. DYNAMAP has a statistical structure and this implies that information captured by each sensor must be representative of an extended area, thus uncorrelated from other stations. The study of the correlations among the sensors represents a key-point in designing the monitoring network. Another important aspect regards the “contemporaneity” of noise fluctuations predicted by DYNAMAP with those effectively measured at an arbitrary location. Integration times heavily affect the result, with correlation coefficients up to 0.8-0.9 for updating times of 1h. Higher correlations are observed when averaging over groups of roads with similar traffic flow characteristics. © 2020, North Atlantic University Union. All rights reserved.
引用
收藏
页码:533 / 541
页数:8
相关论文
共 11 条
  • [1] Noise at the time of COVID 19: The impact in some areas in Rome and Milan, Italy
    Alsina Pages, Rosa Maria
    Alias, Francesc
    Bellucci, Patrizia
    Paolo Cartolano, Pier
    Coppa, Ilaria
    Peruzzi, Laura
    Bisceglie, Alessandro
    Zambon, Giovanni
    NOISE MAPPING, 2020, 7 (01): : 248 - 264
  • [2] The mobility network of scientists: analyzing temporal correlations in scientific careers
    Vaccario, Giacomo
    Verginer, Luca
    Schweitzer, Frank
    APPLIED NETWORK SCIENCE, 2020, 5 (01)
  • [3] The mobility network of scientists: analyzing temporal correlations in scientific careers
    Giacomo Vaccario
    Luca Verginer
    Frank Schweitzer
    Applied Network Science, 5
  • [4] A Pufferfish privacy mechanism for monitoring web browsing behavior under temporal correlations
    Liang, Wenjuan
    Chen, Hong
    Liu, Ruixuan
    Wu, Yuncheng
    Li, Cuiping
    COMPUTERS & SECURITY, 2020, 92 (92)
  • [5] PREDICTION OF URBAN TRAFFIC NOISE USING ARTIFICIAL NEURAL NETWORK APPROACH
    Kumar, Kranti
    Parida, Manoranjan
    Katiyar, Vinod Kumar
    ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, 2014, 13 (04): : 817 - 826
  • [6] Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review
    Picaut, Judicael
    Can, Arnaud
    Fortin, Nicolas
    Ardouin, Jeremy
    Lagrange, Mathieu
    SENSORS, 2020, 20 (08)
  • [7] Analysing urban traflc volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease
    Aletta, Francesco
    Brinchi, Stefano
    Carrese, Stefano
    Gemma, Andrea
    Guattari, Claudia
    Mannini, Livia
    Patella, Sergio Maria
    NOISE MAPPING, 2020, 7 (01): : 114 - 122
  • [8] Risk of Coinfection Outbreaks in Temporal Networks: A Case Study of a Hospital Contact Network
    Rodriguez, Jorge P.
    Ghanbarnejad, Fakhteh
    Eguiluz, Victor M.
    FRONTIERS IN PHYSICS, 2017, 5
  • [9] Noise pollution prediction and seasonal comparison in urban parks using a coupled GIS- artificial neural network model
    Tashakor, Shahla
    Chamani, Atefeh
    Moshtaghie, Minoo
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (02)
  • [10] Noise pollution prediction and seasonal comparison in urban parks using a coupled GIS- artificial neural network model
    Shahla Tashakor
    Atefeh Chamani
    Minoo Moshtaghie
    Environmental Monitoring and Assessment, 2023, 195