A neural network noise prediction model for Tehran urban roads
Abstract
Over the last decades, the number of motor vehicles has increased dramatically in Iran, where different traffic characteristics and urban structures are notable. In the present study, a multilayer perceptron neural network model trained with the Levenberg-Marquardt algorithm was used for predicting the equivalent sound level (LAeq) originating from traffic. Fifty-one samples were collected from different areas of Tehran. Input parameters consisted of total traffic volume per hour, average speed of vehicles, percentage of each category of vehicles, road gradient, density of buildings around the road section and a new parameter named “Building Reflection Factor”. These data were randomly used with 80, 10 and 10 percentiles respectively for training, validation and testing of the Artificial Neural Network (ANN). Results yielded by the ANN model were compared with field measurement data, a proposed regression model and some classical well-known models. Our study indicated that the prediction error of the neural network model was much less than that of the regression model and other classical models. Moreover, a statistical t-test was applied for evaluating the goodness-of-fit of the proposed model and proved that the neural network model is highly efficient in estimating road traffic noise levels.
Keyword : artificial neural network, ANN, traffic noise prediction, modeling, building reflection, building density
How to Cite
Mansourkhaki, A., Berangi, M., Haghiri, M., & Haghani, M. (2018). A neural network noise prediction model for Tehran urban roads. Journal of Environmental Engineering and Landscape Management, 26(2), 88-97. https://doi.org/10.3846/16486897.2017.1356327
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Steele, C. 2001. A critical review of some traffic noise prediction models, Applied Acoustics 62(3): 271–287. https://doi.org/10.1016/S0003-682X(00)00030-X
Bastián-Monarca, N.; Suárez, E.; Arenas, J. 2016. Assessment of methods for simplified traffic noise mapping of small cities: casework of the city of Valdivia, Chile, Science of the Total Environment 550: 439–448. https://doi.org/10.1016/j.scitotenv.2016.01.139
Brink, M. 2011. Parameters of well-being and subjective health and their relationship with residential traffic noise exposure – a representative evaluation in Switzerland, Environment International 37(4): 723–733. https://doi.org/10.1016/j.envint.2011.02.011
Caciari, T.; Rosati, M.; Casale, T.; Loreti, B.; Sancini, A.; Riservato, R.; Nieto, H.; Frati, P.; Tomei, F.; Tomei, G. 2013. Noiseinduced hearing loss in workers exposed to urban stressors, Science of the Total Environment 463: 302–308. https://doi.org/10.1016/j.scitotenv.2013.06.009
Cammarata, G.; Cavalieri, S.; Fichera, A. 1995. A neural network architecture for noise prediction, Neural Networks 8(6): 963–973. https://doi.org/10.1016/0893-6080(95)00016-S
Delany, M. E.; Harland, D. G.; Hood, R. A.; Scholes, W. E. 1976. The prediction of noise levels L10 due to road traffic, Journal of Sound and Vibration 48(3): 305–325. https://doi.org/10.1016/0022-460X(76)90057-2
Demuth, H.; Beale, M. 1998. Neural network toolbox for use with MATLAB. Natick, Mass.: MathWorks, Inc.
Dintrans, A.; Préndez, M. 2013. A method of assessing measures to reduce road traffic noise: a case study in Santiago, Chile, Applied Acoustics 74(12): 1486–1491. https://doi.org/10.1016/j.apacoust.2013.06.012
Euro WHO. 2015. Data and statistics [online], [cited 20 November 2015]. Available from Internet: http://www.euro.who.int/en/health-topics/environment-and-health/noise/data-andstatistics
Fausett, L. 1994. Fundamentals of neural networks. Englewood Cliffs, NJ: Prentice-Hall.
Fyhri, A.; Klboe, R. 2009. Road traffic noise, sensitivity, annoyance and self-reported health – a structural equation model exercise, Environment International 35(1): 91–97. https://doi.org/10.1016/j.envint.2008.08.006
Garg, N.; Maji, S. 2014. A critical review of principal traffic noise models: strategies and implications, Environmental Impact Assessment 46: 68–81. https://doi.org/10.1016/j.eiar.2014.02.001
Genaro, N.; Torija, A.; Ramos, A.; Requena, I.; Ruiz, D.; Zamorano, M. 2009. Modeling environmental noise using artificial neural networks, in 9th International Conference on Intelligent Systems Design and Applications, 30 November–2 December 2009 (ISDA 2009), Pisa, Italy. IEEE, 215–219. https://doi.org/10.1109/ISDA.2009.179
Givargis, S.; Karimi, H. 2010. A basic neural traffic noise prediction model for Tehran’s roads, Journal of Environmental Management 91(12): 2529–2534. https://doi.org/10.1016/j. jenvman.2010.07.011
Guarnaccia, C.; Lenza, T. L. L.; Mastorakis, N. E.; Quartieri, J. 2011. A comparison between traffic noise experimental data and predictive models results, International Journal of Mechanical Sciences 5(4): 379–386.
Haykin, S. 1999. Neural networks: a comprehensive foundation. Prentice-Hall, NJ.
İlgürel, N.; Yüğrük Akdağ, N.; Akdağ, A. 2016. Evaluation of noise exposure before and after noise barriers, a simulation study in Istanbul, Journal of Environmental Engineering and Landscape Management 24(4): 293–302. https://doi.org/10.3846/16486897.2016.1184671
ISO 362:1998. Measurement of noise emitted by accelerating road vehicles. International Organization for Standardization, Geneva.
Johnson, D. R.; Saunders, E. 1968. The evaluation of noise from freely flowing road traffic, Journal of Sound and Vibration 7(2): 287–309. https://doi.org/10.1016/0022-460X(68)90273-3
Kumar, P.; Nigam, S.; Kumar, N. 2014. Vehicular traffic noise modeling using artificial neural network approach, Transportation Research Part C: Emerging Technologies 40: 111–122. https://doi.org/10.1016/j.trc.2014.01.006
Levenberg, K. 1944. A method for the solution of certain nonlinear problems in least squares, Quarterly Journal of Applied Mathematics 2(2): 164–168. https://doi.org/10.1090/qam/10666
Management and Planning Organization of Iran. 2006. Issue No. 342: acoustical guidelines for reduction of traffic noise for buildings near highways. Tehran.
Marquardt, D. 1963. An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics 11(2): 431–441. https://doi.org/10.1137/0111030
Montgomery, D.; Runger, G. 2004. Applied statistics and probability for engineers. 3rd ed. New York: John Wiley & Sons, 349–355.
Nedic, V.; Despotovic, D.; Cvetanovic, S.; Despotovic, M.; Babic, S. 2014. Comparison of classical statistical methods and artificial neural network in traffic noise prediction, Environmental Impact Assessment Review 49: 24–30. https://doi.org/10.1016/j.eiar.2014.06.004
Pamanikabud, P.; Vivitjinda, P. 2002. Noise prediction for highways in Thailand, Transportation Research Part D: Transport and Environment 7(6): 441–449. https://doi.org/10.1016/S1361-9209(02)00012-3
Parabat, D. K.; Nagarnaik, P. B. 2008. Artificial neural network of road traffic noise descriptors, in the 1rst International Conference on Emerging Trends in Engineering and Technology, July 16–18, 2008, Nagpur, Maharashtra, India, 1017–1021.
Paulauskas, L.; Klimas, R. 2011. Modeling of the spread of motor transport noise in Šiauliai city, Journal of Environmental Engineering and Landscape Management 19(1): 62–70. https://doi.org/10.3846/16486897.2011.557249
Pirrera, S.; De Valck, E.; Cluydts, R. 2010. Nocturnal road traffic noise: a review on its assessment and consequences on sleep and health, Environment International 36(5): 492–498. https://doi.org/10.1016/j.envint.2010.03.007
Quartieri, J.; Mastorakis, N.; Iannone, G.; Guarnaccia, C.; D’Ambrosio, S.; Troisi, A.; Lenza, T. L. L. 2009. A review of traffic noise predictive models, in the 5th WSEAS International Conference on Applied and Theoretical Mechanics, 14–16 December 2009, Puerto De La Cruz, Canary Islands, Spain.
StatSoft, Inc. 2013. Model extremely complex functions, neural networks [online], [cited 05 July 2017]. Available from Internet: http://www.statsoft.com/Textbook/Neural-Networks
Steele, C. 2001. A critical review of some traffic noise prediction models, Applied Acoustics 62(3): 271–287. https://doi.org/10.1016/S0003-682X(00)00030-X