Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system

  • Ahmed Abdulkareem Ahmed
  • Biswajeet PradhanEmail author


This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.


Traffic noise Noise prediction Noise propagation Neural networks Remote sensing GIS LiDAR 



The authors acknowledge and appreciate the provision of airborne laser scanning data (LiDAR), satellite images and logistic support by the PLUS Berhad. Thanks to anonymous reviewers for their valuable feedback which helped us to improve the quality of the manuscript.

Funding information

In addition, the second author, Biswajeet Pradhan, gratefully acknowledges the financial support from the UPM-PLUS industry project grant. This research is supported by the UTS under grant numbers 321740.2232335 and 321740.2232357.

Supplementary material

10661_2019_7333_MOESM1_ESM.docx (2.6 mb)
ESM 1 (DOCX 2667 kb)


  1. Aguilera, I., Foraster, M., Basagaña, X., Corradi, E., Deltell, A., Morelli, X., ... & Slama, R. (2015). Application of land use regression modelling to assess the spatial distribution of road traffic noise in three European cities. Journal of Exposure Science and Environmental Epidemiology, 25(1), 97–105.Google Scholar
  2. Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B., & Pour, A. (2018). A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sensing, 10(6), 975.CrossRefGoogle Scholar
  3. Azeez, O., Pradhan, B., & Shafri, H. (2018). Vehicular CO emission prediction using support vector regression model and GIS. Sustainability, 10(10), 3434.CrossRefGoogle Scholar
  4. Azeez, O., Pradhan, B., Shafri, H., Shukla, N., Lee, C.-W., & Rizeei, H. (2019). Modeling of CO emissions from traffic vehicles using artificial neural networks. Applied Sciences, 9(2), 313.CrossRefGoogle Scholar
  5. Baczyński, D., & Parol, M. (2004). Influence of artificial neural network structure on quality of short-term electric energy consumption forecast. IEE Proceedings-Generation, Transmission and Distribution, 151(2), 241–245.CrossRefGoogle Scholar
  6. Calixto, A., Diniz, F. B., & Zannin, P. H. (2003). The statistical modeling of road traffic noise in an urban setting. Cities, 20(1), 23–29.CrossRefGoogle Scholar
  7. Cetin, M. (2015). Using GIS analysis to assess urban green space in terms of accessibility: case study in Kutahya. International Journal of Sustainable Development & World Ecology, 22(5), 420–424 Scholar
  8. Cetin, M. (2016). Sustainability of urban coastal area management: a case study on Cide. Journal of Sustainable Forestry, 2016, 35(7), 527–541. Scholar
  9. Cetin, M., Topay, M., Kaya, L. G., & Yilmaz, B. (2010). Efficiency of bioclimatic comfort in landscape planning process: case of Kutahya. Turkish Journal of Forestry, 1(1), 83–95.Google Scholar
  10. Cetin, M., Zeren, I., Sevik, H., Cakir, C., & Akpinar, H. (2018). A study on the determination of the natural park’s sustainable tourism potential. Environmental Monitoring and Assessment, 190(3), 167.CrossRefGoogle Scholar
  11. Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14), 2627–2636.CrossRefGoogle Scholar
  12. Garg, N., & Maji, S. (2014). A critical review of principal traffic noise models: strategies and implications. Environmental Impact Assessment Review, 46, 68–81.CrossRefGoogle Scholar
  13. Hamad, K., Khalil, M. A., & 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.CrossRefGoogle Scholar
  14. Kaya E, Agca M, Adiguzel F, & Cetin M (2018). Spatial data analysis with R programming for environment. Human and Ecological Risk Assessment: An International Journal, 1-10. doi
  15. Kumar, P., Nigam, S. P., & Kumar, N. (2014). Vehicular traffic noise modeling using artificial neural network approach. Transportation Research Part C: Emerging Technologies, 40, 111–122.CrossRefGoogle Scholar
  16. Kumar, K., Parida, M., & Katiyar, V. K. (2015). Short term traffic flow prediction in heterogeneous condition using artificial neural network. Transport, 30(4), 397–405.CrossRefGoogle Scholar
  17. Lee, C. S., & Fleming, G. G. (2002). General health effects of transportation noise. US Department of Transportation. Accessed 23 Feb 2018.
  18. Li, F., Liao, S. S., & Cai, M. (2016). A new probability statistical model for traffic noise prediction on free flow roads and control flow roads. Transportation Research Part D: Transport and Environment, 49, 313–322.CrossRefGoogle Scholar
  19. Mokhtarzade, M., & Zoej, M. V. (2007). Road detection from high-resolution satellite images using artificial neural networks. International Journal of Applied Earth Observation and Geoinformation, 9(1), 32–40.CrossRefGoogle Scholar
  20. Oh, H.-J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37(9), 1264–1276.CrossRefGoogle Scholar
  21. Ozer, S., Yilmaz, H., Yeşil, M., & Yeşil, P. (2009). Evaluation of noise pollution caused by vehicles in the city of Tokat, Turkey. Scientific Research and Essays, 4(11), 1205–1212.Google Scholar
  22. Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.CrossRefGoogle Scholar
  23. Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747–759.CrossRefGoogle Scholar
  24. Priddy, K. L., & Keller, P. E. (2005). Artificial neural networks: an introduction (vol. 68, pp. 180). Bellingham: SPIE Press.Google Scholar
  25. Ragettli, M. S., Goudreau, S., Plante, C., Fournier, M., Hatzopoulou, M., Perron, S., & Smargiassi, A. (2016). Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics. Journal of Exposure Science and Environmental Epidemiology, 26, 597–605.CrossRefGoogle Scholar
  26. Rahmani, S., Mousavi, S. M., & Kamali, M. J. (2011). Modeling of road-traffic noise with the use of genetic algorithm. Applied Soft Computing, 11(1), 1008–1013.CrossRefGoogle Scholar
  27. Ranjbar, H. R., Gharagozlou, A. R., & Nejad, A. R. V. (2012). 3D analysis and investigation of traffic noise impact from Hemmat highway located in Tehran on buildings and surrounding areas. Journal of Geographic Information System, 4(4), 322–334.CrossRefGoogle Scholar
  28. Sameen, M., & Pradhan, B. (2017). Severity prediction of traffic accidents with recurrent neural networks. Applied Sciences, 7(6), 476.CrossRefGoogle Scholar
  29. Sameen, M. I., Pradhan, B., & Aziz, O. S. (2018). Classification of very high resolution aerial photos using spectral-spatial convolutional neural networks. Journal of Sensors, 2018, 1–12.CrossRefGoogle Scholar
  30. Sameen M. I., Pradhan B., Shafri H. Z. M., & Hamid H. B. (2019). Applications of deep learning in severity prediction of traffic accidents. In: B. Pradhan (Ed.), GCEC 2017. Lecture Notes in Civil Engineering (vol. 9). Singapore: Springer.
  31. Sebri, M. (2013). ANN versus SARIMA models in forecasting residential water consumption in Tunisia. Journal of Water Sanitation and Hygiene for Development, 3(3), 330–340.CrossRefGoogle Scholar
  32. Singh, D., Nigam, S. P., Agrawal, V. P., & Kumar, M. (2016). Vehicular traffic noise prediction using soft computing approach. Journal of Environmental Management, 183, 59–66.CrossRefGoogle Scholar
  33. Stansfeld, S. A., & Matheson, M. P. (2003). Noise pollution: non-auditory effects on health. British Medical Bulletin, 68(1), 243–257.CrossRefGoogle Scholar
  34. Steele, C. (2001). A critical review of some traffic noise prediction models. Applied Acoustics, 62(3), 271–287.CrossRefGoogle Scholar
  35. Stoter, J., De Kluijver, H., & Kurakula, V. (2008). 3D noise mapping in urban areas. International Journal of Geographical Information Science, 22(8), 907–924.CrossRefGoogle Scholar
  36. Tomić, J., Bogojević, N., Pljakić, M., & Šumarac-Pavlović, D. (2016). Assessment of traffic noise levels in urban areas using different soft computing techniques. The Journal of the Acoustical Society of America, 140(4), EL340–EL345.CrossRefGoogle Scholar
  37. Turkyilmaz, A., Sevik, H., Isinkaralar, K., & Cetin, M. (2018). Using Acer platanoides annual rings to monitor the amount of heavy metals accumulated in air. Environmental Monitoring and Assessment, 190, 578. Scholar
  38. Yucedag, C., Kaya, L. G., & Cetin, M. (2018). Identifying and assessing environmental awareness of hotel and restaurant employees’ attitudes in the Amasra District of Bartin. Environmental Monitoring and Assessment, 190(2), 60.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information TechnologyUniversity of TechnologySydneyAustralia

Personalised recommendations