Estimating urban noise along road network from street view imagery

References

• Al-Ghonamy, A., 2009. Analysis and evaluation of road traffic noise in Al-Dammam: a business city of the eastern province of KSA. Journal of Environmental Science and Technology, 3 (1), 47–55.
   Google Scholar
• Alam, P., et al., 2020. Noise monitoring, mapping, and modelling studies–a review. Journal of Ecological Engineering, 21 (4), 82–93.
   Web of Science ®Google Scholar
• Anguelov, D., et al., 2010. Google street view: capturing the world at street level. Computer Magazine., 43 (6), 32–38.
   Web of Science ®Google Scholar
• Asfiati, S., et al., 2020. Measurement and evaluation of sound intensity at the Medan Railway Station using a sound level meter. ed. Journal of Physics: Conference Series, 1428 (1), 012063.
   Google Scholar
• Aumond, P., et al., 2018. Kriging-based spatial interpolation from measurements for sound level mapping in urban areas. The Journal of the Acoustical Society of America, 143 (5), 2847–2857.
   PubMed Web of Science ®Google Scholar
• Banerjee, D., et al., 2008. Evaluation and analysis of road traffic noise in Asansol: an industrial town of eastern India. International Journal of Environmental Research and Public Health, 5 (3), 165–171.
   PubMedGoogle Scholar
• Barrigón Morillas, J.M., et al., 2005. A categorization method applied to the study of urban road traffic noise. The Journal of the Acoustical Society of America, 117 (5), 2844–2852.
   PubMed Web of Science ®Google Scholar
• Barry, T.M., and Reagan, J.A., 1978. FHWA highway traffic noise prediction model. Washington, DC: Department of Transportation.
   Google Scholar
• Biljecki, F., and Ito, K., 2021. Street view imagery in urban analytics and GIS: a review. Landscape and Urban Planning, 215, 104217.
   Web of Science ®Google Scholar
• Brambilla, G., et al., 2020. Classification of urban road traffic noise based on sound energy and eventfulness indicators. Applied Sciences, 10 (7), 2451.
   Google Scholar
• Bravo-Moncayo, L., et al., 2019. A machine learning approach for traffic-noise annoyance assessment. Applied Acoustics, 156, 262–270.
   Web of Science ®Google Scholar
• Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32.
   Web of Science ®Google Scholar
• Brockfeld, E., et al., 2007. Benefits and limits of recent floating car data technology–an evaluation study. In: Proceedings of the 11th WCTR conference, Berkeley, CA, 24–28.
   Google Scholar
• Can, A., Dekoninck, L., and Botteldooren, D., 2014. Measurement network for urban noise assessment: Comparison of mobile measurements and spatial interpolation approaches. Applied Acoustics, 83, 32–39.
   Web of Science ®Google Scholar
• Carmona del Río, F.J., et al., 2011. A street categorization method to study urban noise: The Valladolid (Spain) study. Environmental Engineering Science, 28 (11), 811–817.
   Web of Science ®Google Scholar
• Chen, L., et al., 2021. Modelling traffic noise in a wide gradient interval using artificial neural networks. Environmental Technology, 42 (23), 3561–3571.
   PubMed Web of Science ®Google Scholar
• Cohen, B.S., et al., 2008. Airport-related air pollution and noise. Journal of Occupational and Environmental Hygiene, 5 (2), 119–129.
   PubMed Web of Science ®Google Scholar
• Cortes, C., and Vapnik, V., 1995. Support-vector networks. Machine Learning, 20 (3), 273–297.
   Web of Science ®Google Scholar
• Doygun, H., and Kuşat Gurun, D., 2008. Analysing and mapping spatial and temporal dynamics of urban traffic noise pollution: a case study in Kahramanmaraş, Turkey. Environmental Monitoring and Assessment, 142 (1–3), 65–72.
   PubMed Web of Science ®Google Scholar
• Fan, Z., et al., 2023. Urban visual intelligence: uncovering hidden city profiles with street view images. Proceedings of the National Academy of Sciences of the United States of America, 120 (27), e2220417120.
   PubMed Web of Science ®Google Scholar
• Fang, C.-F., and Ling, D.-L., 2003. Investigation of the noise reduction provided by tree belts. Landscape and Urban Planning, 63 (4), 187–195.
   Web of Science ®Google Scholar
• Flores, A. and Belongie, S., 2010. Removing pedestrians from google street view images. In: 2010 IEEE computer society conference on computer vision and pattern recognition-workshops. New York, NY: IEEE, 53–58.
   Google Scholar
• Freitas, E., et al., 2012. Traffic noise abatement: How different pavements, vehicle speeds and traffic densities affect annoyance levels. Transportation Research Part D: Transport and Environment, 17 (4), 321–326.
   Web of Science ®Google Scholar
• Frome, A., et al., 2009. Large-scale privacy protection in google street view. In: 2009 IEEE 12th International Conference on Computer Vision. Kyoto, Japan: IEEE, 2373–2380.
   Google Scholar
• Gao, W., et al., 2015. Assessment on road traffic noise in the Xizhimen overpass area. In: Proceedings of the 15th COTA international conference of transportation professionals. Beijing, China: American Society of Civil Engineers (ASCE), 3290–3299.
   Google Scholar
• Garg, N., and Maji, S., 2014. A critical review of principal traffic noise models: strategies and implications. Environmental Impact Assessment Review, 46, 68–81.
   Web of Science ®Google Scholar
• Gershon, R.R., et al., 2006. Pilot survey of subway and bus stop noise levels. Journal of Urban Health , 83 (5), 802–812.
   PubMed Web of Science ®Google Scholar
• Ghotbi, M.R., et al., 2012. Noise pollution survey of a two-storey intersection station in Tehran metropolitan subway system. Environmental Monitoring and Assessment, 184 (2), 1097–1106.
   PubMed Web of Science ®Google Scholar
• Givargis, S., and Mahmoodi, M., 2008. Converting the UK calculation of road traffic noise (CORTN) to a model capable of calculating LAeq, 1h for the Tehran’s roads. Applied Acoustics, 69 (11), 1108–1113.
   Web of Science ®Google Scholar
• Gong, F.-Y., et al., 2018. Mapping sky, tree, and building view factors of street canyons in a high-density urban environment. Building and Environment, 134, 155–167.
   Web of Science ®Google Scholar
• Hamad, K., Khalil, M.A., and Shanableh, A., 2017. Modeling roadway traffic noise in a hot climate using artificial neural networks. Transportation Research Part D: Transport and Environment, 53, 161–177.
   Web of Science ®Google Scholar
• Harman, B.I., Koseoglu, H., and Yigit, C.O., 2016. Performance evaluation of IDW, Kriging and multiquadric interpolation methods in producing noise mapping: A case study at the city of Isparta, Turkey. Applied Acoustics, 112, 147–157.
   Web of Science ®Google Scholar
• He, K., et al., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV: IEEE, 770–778.
   Google Scholar
• Heutschi, K., 2004. SonRoad: New Swiss road traffic noise model. Acta Acustica United with Acustica, 90 (3), 548–554.
   Web of Science ®Google Scholar
• Hu, C.-B., et al., 2020. Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning. Building and Environment, 167, 106424.
   Web of Science ®Google Scholar
• Ji, H., et al., 2021. A new data-enabled intelligence framework for evaluating urban space perception. ISPRS International Journal of Geo-Information, 10 (6), 400.
   Web of Science ®Google Scholar
• Jokar Arsanjani, J., et al., 2015. An introduction to OpenStreetMap in geographic information science: experiences, research, and applications. In: J. Jokar Arsanjani, et al. eds. OpenStreetMap in GIScience. Cham, Switzerland: Springer, 1–15.
   Google Scholar
• Kang, Y., et al., 2020. A review of urban physical environment sensing using street view imagery in public health studies. Annals of GIS, 26 (3), 261–275.
   Web of Science ®Google Scholar
• Kang, Y., et al., 2021. Human settlement value assessment from a place perspective: Considering human dynamics and perceptions in house price modeling. Cities, 118, 103333.
   Web of Science ®Google Scholar
• Kang, Y., et al., 2023. Assessing differences in safety perceptions using GeoAI and survey across neighbourhoods in Stockholm, Sweden. Landscape and Urban Planning, 236, 104768.
   Web of Science ®Google Scholar
• Khan, J., et al., 2018. Road traffic air and noise pollution exposure assessment–a review of tools and techniques. The Science of the Total Environment, 634, 661–676.
   PubMed Web of Science ®Google Scholar
• Kim, M., et al., 2012. Road traffic noise: annoyance, sleep disturbance, and public health implications. American Journal of Preventive Medicine, 43 (4), 353–360.
   PubMed Web of Science ®Google Scholar
• Li, B., Tao, S., and Dawson, R., 2002. Evaluation and analysis of traffic noise from the main urban roads in Beijing. Applied Acoustics, 63 (10), 1137–1142.
   Web of Science ®Google Scholar
• Li, X., et al., 2015. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry & Urban Greening, 14 (3), 675–685.
   Web of Science ®Google Scholar
• Loshchilov, I., and Hutter, F., 2016. Sgdr: stochastic gradient descent with warm restarts. arXiv Preprint arXiv:1608.03983. https://doi.org/10.48550/arXiv.1608.03983.
   Google Scholar
• Ma, X., et al., 2017. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors, 17 (4), 818.
   PubMed Web of Science ®Google Scholar
• Mansouri, N., Pourmahabadian, M., and Ghasemkhani, M., 2006. Road traffic noise in downtown area of Tehran. Journal of Environmental Health Science & Engineering, 3 (4), 267–272.
   Google Scholar
• Mehdi, M.R., et al., 2011. Spatio-temporal patterns of road traffic noise pollution in Karachi, Pakistan. Environment International, 37 (1), 97–104.
   PubMed Web of Science ®Google Scholar
• Murphy, E., and King, E.A., 2010. Strategic environmental noise mapping: methodological issues concerning the implementation of the EU environmental noise directive and their policy implications. Environment International, 36 (3), 290–298.
   PubMed Web of Science ®Google Scholar
• Nagrani, A., Albanie, S., and Zisserman, A., 2018. Seeing voices and hearing faces: Cross-modal biometric matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, UT: IEEE, 8427–8436.
   Google Scholar
• Nedic, V., et al., 2014. Comparison of classical statistical methods and artificial neural network in traffic noise prediction. Environmental Impact Assessment Review, 49, 24–30.
   Web of Science ®Google Scholar
• Neitzel, R., et al., 2009. Noise levels associated with New York City’s mass transit systems. American Journal of Public Health, 99 (8), 1393–1399.
   PubMed Web of Science ®Google Scholar
• Okonofua, E., Ehigiator-Irughe, R., and Ekun, M., 2016. Traffic noise monitoring at road intersections in urban settlement: case study of Ramat Park Benin City. Nigerian Journal of Technology, 35 (4), 713–717.
   Google Scholar
• Onder, S., and Kocbeker, Z., 2012. Importance of the green belts to reduce noise pollution and determination of roadside noise reduction effectiveness of bushes in Konya, Turkey. International Journal of Agricultural and Biosystems Engineering, 6 (6), 373–376.
   Google Scholar
• Ow, L.F., and Ghosh, S., 2017. Urban cities and road traffic noise: reduction through vegetation. Applied Acoustics, 120, 15–20.
   Web of Science ®Google Scholar
• Paiva, K.M., Cardoso, M.R.A., and Zannin, P.H.T., 2019. Exposure to road traffic noise: annoyance, perception and associated factors among Brazil’s adult population. The Science of the Total Environment, 650 (Pt 1), 978–986.
   PubMedGoogle Scholar
• Peppa, M., et al., 2018. Urban traffic flow analysis based on deep learning car detection from CCTV image series. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 499–506.
   Google Scholar
• Qian, Z., et al., 2022. Vectorized dataset of roadside noise barriers in China using street view imagery. Earth System Science Data, 14 (9), 4057–4076.
   Web of Science ®Google Scholar
• Quartieri, J., et al., 2009, December. A review of traffic noise predictive models. Recent advances in applied and theoretical mechanics, 5th WSEAS International Conference on Applied and Theoretical Mechanics (MECHANICS’09), Puerto De La Cruz, Tenerife, Canary Islands, Spain, 14–16.
   Google Scholar
• Rahmani, M., Jenelius, E., and Koutsopoulos, H.N., 2013. Route travel time estimation using low-frequency floating car data. In: Proceedings of the 16th international IEEE conference on intelligent transportation systems (ITSC 2013). Piscataway, NJ: IEEE, 2292–2297.
   Google Scholar
• Seber, G., and Wild, C., 1989. Nonlinear regression. New York: Wiley, 768.
   Google Scholar
• Singh, D., et al., 2021. Application of machine learning to include honking effect in vehicular traffic noise prediction. Applied Sciences, 11 (13), 6030.
   Google Scholar
• Singh, N., and Davar, S.C., 2004. Noise pollution-sources, effects and control. Journal of Human Ecology, 16 (3), 181–187.
   Google Scholar
• Son, H.-J., et al., 2014. A study of traffic noise characteristic of pavement types using NCPX method. In: INTER-NOISE and NOISE-CON congress and conference proceedings. Melbourne, Australia: Institute of Noise Control Engineering, 3313–3318.
   Google Scholar
• Stamos, I., et al., 2016. Modeling effects of precipitation on vehicle speed: floating car data approach. Transportation Research Record: Journal of the Transportation Research Board, 2551 (1), 100–110.
   Google Scholar
• Tah-Chew, T., and Keung, W.C., 1991. Noise surveys of hand-held pneumatic rock drills in Hong Kong. Environmental Monitoring and Assessment, 19 (1–3), 519–523.
   PubMed Web of Science ®Google Scholar
• Talotte, C., et al., 2003. Identification, modelling and reduction potential of railway noise sources: a critical survey. Journal of Sound and Vibration, 267 (3), 447–468.
   Web of Science ®Google Scholar
• Trombetta Zannin, P.H., and Bunn, F., 2014. Noise annoyance through railway traffic-a case study. Journal of Environmental Health Science & Engineering, 12 (1), 14.
   PubMedGoogle Scholar
• Van Renterghem, T., Botteldooren, D., and Verheyen, K., 2012. Road traffic noise shielding by vegetation belts of limited depth. Journal of Sound and Vibration, 331 (10), 2404–2425.
   Web of Science ®Google Scholar
• Wallis, A.D., 1997. Airport noise monitoring‐a review. Aircraft Engineering and Aerospace Technology, 69 (2), 112–118.
   Web of Science ®Google Scholar
• Wang, K., et al., 2016. A comprehensive survey on cross-modal retrieval. arXiv Preprint arXiv:1607.06215. https://doi.org/10.48550/arXiv.1607.06215.
   Google Scholar
• Watts, G., 2005. Harmonoise prediction model for road traffic noise. Wokingham: TRL.
   Google Scholar
• Wei, Y., et al., 2017. Cross-modal retrieval with CNN visual features: a new baseline. IEEE Transactions on Cybernetics, 47 (2), 449–460.
   PubMed Web of Science ®Google Scholar
• Wu, H., et al., 2019. A CNN-SVM combined regression model for continuous knee angle estimation using mechanomyography signals. 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 124–131.
   Google Scholar
• Yamamoto, K., 2010. Road traffic noise prediction model “ASJ RTN-Model 2008”: report of the research committee on road traffic noise. Acoustical Science and Technology, 31 (1), 2–55.
   Google Scholar
• Ye, C., et al., 2021. Urban function recognition by integrating social media and street-level imagery. Environment and Planning B: Urban Analytics and City Science, 48 (6), 1430–1444.
   Google Scholar
• Yilmaz, G., and Hocanli, Y., 2006. Mapping of noise by using GIS in Şanliurfa. Environmental Monitoring and Assessment, 121 (1–3), 103–108.
   PubMed Web of Science ®Google Scholar
• Yin, L., et al., 2015. Big data’for pedestrian volume: exploring the use of Google Street View images for pedestrian counts. Applied Geography, 63, 337–345.
   Web of Science ®Google Scholar
• Zambon, G., Benocci, R., and Brambilla, G., 2016. Statistical road classification applied to stratified spatial sampling of road traffic noise in urban areas. International Journal of Environmental Research, 10 (3), 411–420.
   Web of Science ®Google Scholar
• Zeng, L., et al., 2018. A fast approach for large-scale sky view factor estimation using street view images. Building and Environment, 135, 74–84.
   Web of Science ®Google Scholar
• Zhang, F., et al., 2019a. Social sensing from street-level imagery: a case study in learning spatio-temporal urban mobility patterns. ISPRS Journal of Photogrammetry and Remote Sensing, 153, 48–58.
   Web of Science ®Google Scholar
• Zhang, K., et al., 2022. Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis. Sustainable Cities and Society, 78, 103598.
   Web of Science ®Google Scholar
• Zhang, W., et al., 2018. Using deep learning to identify utility poles with crossarms and estimate their locations from Google Street View images. Sensors, 18 (8), 2484.
   PubMed Web of Science ®Google Scholar
• Zhang, W., et al., 2019b. Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A: Transport Science, 15 (2), 1688–1711.
   Web of Science ®Google Scholar
• Zhao, T., et al., 2023. Sensing urban soundscapes from street view imagery. Computers, Environment and Urban Systems, 99, 101915.
   Web of Science ®Google Scholar
• Zhong, T., et al., 2021. Assessment of solar photovoltaic potentials on urban noise barriers using street-view imagery. Renewable Energy., 168, 181–194.
   Web of Science ®Google Scholar
• Zhou, B., et al., 2016. Learning deep features for discriminative localization. Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV: IEEE, 2921–2929.
   Google Scholar