Skip to main content

Advertisement

Log in

Sound measurement and automatic vehicle classification and counting applied to road traffic noise characterization

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

A Correction to this article was published on 07 May 2021

This article has been updated

Abstract

Increase in population density in large cities has increased the environmental noise present in these environments, causing negative effects on human health. There are different sources of environmental noise; however, noise from road traffic is the most prevalent in cities. Therefore, it is necessary to have tools that allow noise characterization to establish strategies that permit obtaining levels that do not affect the quality of life of people. This research discusses the implementation of a system that allows the acquisition of data to characterize the noise generated by road traffic. First, the methodology for obtaining acoustic indicators with an electret measurement microphone is described, so that it adjusts to the data collection needs for road traffic noise analyses. Then, an approach for the classification and counting of automatic vehicular traffic through deep learning is presented. Results showed that there were differences of 0.2 dBA in terms of RMSE between a type 1 sound level meter and the measurement microphone used. With reference to vehicle classification and counting for four categories, the approximate error is between 3.3% and -15.5%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Change history

References

  • Acosta OE, Montenegro CE, Gaona P (2020) Condiciones de tránsito vehicular y uso de un modelo para la predicción de ruido por tráfico rodado en un entorno local de la ciudad de Bogotá-Colombia. Iber J Inf Syst Technol 27:605–614

    Google Scholar 

  • Alías F, Alsina-Pagès RM, Orga F, Socoró JC (2018) Detection of anomalous noise events for real-time road-traflc noise mapping: The DYNAMAP’s project case study. Noise Mapp 5:71–85. https://doi.org/10.1515/noise-2018-0006

    Article  Google Scholar 

  • Ardouin J, Charpentier L, Lagrange M, Fortin N, Ecotière D, Guillaume G 2018 An innovative low cost sensor for urban sound monitoring

  • Arinaldi A, Pradana JA, Gurusinga AA (2018) Detection and classification of vehicles for traffic video analytics. Procedia Comput Sci 144:259–268. https://doi.org/10.1016/j.procs.2018.10.527

    Article  Google Scholar 

  • Ausejo M, Recuero M, Asensio C, Pavón I, Pagán R 2010 Study of uncertainty in noise mapping. 39th Int. Congr. Noise Control Eng. 2010, INTER-NOISE 2010 8, 6210–6219.

  • Ballou G (2013) Handbook for sound engineers. Taylor & Francis

  • Basner M, Babisch W, Davis A, Brink M, Clark C, Janssen S, Stansfeld S (2014) Auditory and non-auditory effects of noise on health. Lancet 383:1325–1332. https://doi.org/10.1016/S0140-6736(13)61613-X

    Article  Google Scholar 

  • Bellucci P, Peruzzi L, Zambon G 2018 LIFE DYNAMAP: making dynamic noise maps a reality. 1181–1188

  • Bochkovskiy A, Wang CY, Liao HYM 2020 YOLOv4: Optimal Speed and Accuracy of Object Detection

  • Botteldooren D, De Coensel B, Oldoni D, Van Renterghem T, Dauwe, S 2011 Sound monitoring networks new style. Aust Acoust Soc 2011, Acoust 2011 Break New Gr 1–5

  • Brüel & Kjær (2015) Noise monitoring terminals types 3639 and 3655. 2015

  • Bundesminister für Verkehr (1990) Richtlinien für den Lärmschutz an Strassen RLS-90

  • Can A, Leclercq L, Lelong J, Botteldooren D (2010) Traffic noise spectrum analysis: Dynamic modeling vs. experimental observations. Appl. Acoust 71:764–770. https://doi.org/10.1016/j.apacoust.2010.04.002

    Article  Google Scholar 

  • Commission IE (2013) Others electroacoustics—sound level meters—part 1: specifications (IEC 61672–1). Switz, Geneva

    Google Scholar 

  • Davis L (2019) Sound advisor noise monitoring systems

  • 01dB (2015) CUBE Smart Noise Monitoring Terminal

  • Department of Transport and The Welsh Office (1988) Calculation of Road Traffic Noise

  • European Environmental Agency (2014) Noise in Europe 2014

  • Fu H, Ma H, Liu Y, Lu D (2016) A vehicle classification system based on hierarchical multi-SVMs in crowded traffic scenes. Neurocomputing 211:182–190. https://doi.org/10.1016/j.neucom.2015.12.134

    Article  Google Scholar 

  • Gupta N, Gupta S, Khosravy M, Dey N, Joshi N, Crespo RG, Patel N (2020) Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles. J. Intell. Manuf 32:1–12

    Google Scholar 

  • International Electrotechnical Commission Electroacoustics-Sound level meters-Part 1: Specifications (IEC 61672–1). Geneva, Switz. 2013.

  • International Organization for Standardization (2017) ISO 1996. Attenuation of sound during propagation outdoors - Part 2: General Method of Calculation. 24

  • Jin J, Gubbi J, Marusic S, Palaniswami M (2014) An information framework for creating a smart city through internet of things. IEEE Internet Things J 1:112–121. https://doi.org/10.1109/JIOT.2013.2296516

    Article  Google Scholar 

  • Joshi RC, Singh AG, Joshi M, Mathur S (2019) A low cost and computationally efficient approach for occlusion handling in video surveillance systems. Int J Interact Multimed Artif Intell 5:28. https://doi.org/10.9781/ijimai.2019.01.001

    Article  Google Scholar 

  • Kephalopoulos S, Paviotti M, Anfosso-Lédée F, Van Maercke D, Shilton S, Jones N (2014) Advances in the development of common noise assessment methods in Europe: The CNOSSOS-EU framework for strategic environmental noise mapping. Sci Total Environ 482–483:400–410. https://doi.org/10.1016/J.SCITOTENV.2014.02.031

    Article  Google Scholar 

  • King RP, Davis JR (2003) Community noise: health effects and management. Int J Hyg Environ Health 206:123–131. https://doi.org/10.1078/1438-4639-00202

    Article  Google Scholar 

  • Kuznetsova A, Rom H, Alldrin N et al (2020) The Open Images Dataset V4: Unied Image Classication, Object Detection, and Visual Relationship Detection at Scale. Int J Comput Vis 128:1956–1981. https://doi.org/10.1007/s11263-020-01316-z

  • Lamure C (1986) Road traffic noise: generation, propagation and control Noise Pollution Effects and Control. Wiley, New York

    Google Scholar 

  • Licitra G (2012) Noise Mapping in the EU. CRC Press

  • Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL 2014 Microsoft COCO: Common objects in context. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 8693 LNCS, 740–755, https://doi.org/10.1007/978-3-319-10602-1_48

  • Maheshan MS, Harish BS, Nagadarshan N (2020) A convolution neural network engine for sclera recognition. Int J Interact Multimed Artif Intell 6:78. https://doi.org/10.9781/ijimai.2019.03.006

    Article  Google Scholar 

  • MINIDSP (2020) UMIK-1 Product datasheet. https://www.minidsp.com/images/documents/Product Brief - Umik.pdf

  • Mioduszewski P, Ejsmont JA, Grabowski J, Karpiński D (2011) Noise map validation by continuous noise monitoring. Appl Acoust 72:582–589. https://doi.org/10.1016/j.apacoust.2011.01.012

    Article  Google Scholar 

  • Murphy E, King EA (2014) Environmental Noise Pollution. Elsevier

  • Mydlarz C, Sharma M, Lockerman Y, Steers B, Silva C, Bello JP (2019) The life of a New York City noise sensor network. Sensors. https://doi.org/10.3390/s19061415

    Article  Google Scholar 

  • Open Images Dataset Available online: https://storage.googleapis.com/openimages/web/index.html.

  • Peckens C, Porter C, Rink T (2018) Wireless sensor networks for long-term monitoring of urban noise. Sensors (Switzerland). https://doi.org/10.3390/s18093161

    Article  Google Scholar 

  • Pirrera S, De Valck E, Cluydts R (2010) Nocturnal road traffic noise: a review on its assessment and consequences on sleep and health. Environ Int 36:492–498. https://doi.org/10.1016/j.envint.2010.03.007

    Article  Google Scholar 

  • Planche B, Andres E 2019 Hands-On Computer Vision with TensorFlow 2 ISBN 9781788830645.

  • Recuero M, Gil C, Grundman J 1996 Mapa de ruido de San Sebastián de los Reyes. Metodología de medidas y resultados. Rev Acústica 51–54

  • Recuero M, Gil C, Grundman J (1997) Mapa de ruido de Segovia Estudio de diferentes ambientes acústicos. Tecniacústica 97:29–32

    Google Scholar 

  • Redmon J 2016 Darknet: Open Source Neural Networks in C

  • Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7263–7271

  • Redmon J, Farhadi A 2018 Yolov3: An incremental improvement. arXiv Prepr. arXiv1804.02767

  • Redmon J, Divvala S, Girshick R 2016 Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection

  • Sarikan SS, Ozbayoglu AM, Zilci O (2017) Automated vehicle classification with image processing and computational intelligence. Procedia Comput Sci 114:515–522. https://doi.org/10.1016/j.procs.2017.09.022

    Article  Google Scholar 

  • Sasongko AT, Jati G, Fanany MI, Jatmiko W (2020) Dataset of vehicle images for Indonesia toll road tariff classification. Data Br. https://doi.org/10.1016/j.dib.2020.106061

    Article  Google Scholar 

  • Secretaría de Movilidad de Medellín (2020) SIMM Sistema Inteligente de Movilidad de Medellín. https://www.medellin.gov.co/simm/

  • Séneca (65AD) Epistulae Morales ad Lucilium. Letter 56.

  • SETRA-CERTULCPC-CSTB (1996) NMPB-Routes-96

  • Silva F, Analide C, Novais P (2014) Assessing road traffic expression. IJIMAI 3:20–27

    Article  Google Scholar 

  • Tabassum S, Ullah S, Al-nur NH, Shatabda S (2020) Poribohon-BD: Bangladeshi local vehicle image dataset with annotation for classification. Data Br. https://doi.org/10.1016/j.dib.2020.106465

    Article  Google Scholar 

  • Tzutalin D 2018 LabelImg. GitHub Repos. https//github. com/tzutalin/labelImg

  • United Nations (1972) Declaration of the United Nations conference on the human environment

  • United Nations (1992) Agenda 21: Earth Summit: The United Nations Programme of Action from Rio

  • United Nations (1997) Kyoto protocol to the United Nations framework convention on climate change

  • Verma KK, Singh BM, Mandoria HL, Chauhan P (2020) Two-stage human activity recognition using 2D-ConvNet. Int J Interact Multimed Artif Intell 6:11. https://doi.org/10.9781/ijimai.2020.04.002

    Article  Google Scholar 

  • Vittorio A 2018 Toolkit to download and visualize single or multiple classes from the huge Open Images v4 dataset. GitHub Repos

  • Wessels PW, Basten TGH (2016) Design aspects of acoustic sensor networks for environmental noise monitoring. Appl Acoust 110:227–234. https://doi.org/10.1016/j.apacoust.2016.03.029

    Article  Google Scholar 

  • Zhang F, Li C, Yang F (2019) Vehicle detection in urban traffic surveillance images based on convolutional neural networks with feature concatenation. Sensors (Switzerland). https://doi.org/10.3390/s19030594

    Article  Google Scholar 

Download references

Funding

This research was supported by the University of San Buenaventura Bogotá, which provided acoustic facilities and equipment to perform the measurements, as well as support during Oscar Acosta doctoral training.

Author information

Authors and Affiliations

Authors

Contributions

OA and CM contributed to conceptualization. OA contributed to methodology. OA and CM contributed to software. OA, CM and RG were involved in validation. OA performed formal analysis. OA and CM conducted investigation. RG contributed resources. OA contributed to data curation. OA was involved in writing—original draft preparation. CM and RG were involved in writing—review and editing. OA contributed to visualization. CE and RG did supervision. CM and RG were involved in project administration. CM and RG contributed to funding acquisition. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Rubén González Crespo.

Ethics declarations

Conflict of interest

There is no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by Vicente Garcia Diaz.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original article has been updated: Due to cross reference incorrect.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Agudelo, O.E.A., Marín, C.E.M. & Crespo, R.G. Sound measurement and automatic vehicle classification and counting applied to road traffic noise characterization. Soft Comput 25, 12075–12087 (2021). https://doi.org/10.1007/s00500-021-05766-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05766-6

Keywords

Navigation