A Smart Traffic Noise Prediction Model for Nairobi City, Kenya
Keywords:
Road Traffic Noise, Machine Learning, Smart prediction modelAbstract
Road traffic flow produces an undesirable externality since it distorts the ambient environmental noise, especially in cities. Such nuisance noise poses a risk to the health of the inhabitants. Globally, the combined concert of the forces of urbanization and road transport motorization has intensified the noise pollution challenge; yet, locally adapted predictive tools remain limited. In Nairobi, the capital city of Kenya, Road Traffic Noise (RTN) remains a less understood environmental nuisance. To date, no predictive RTN models have been developed, while established models such as CoRTN and RLS-90 lack applicability to Nairobi’s traffic and environmental conditions. This study aimed to develop an accurate smart model leveraging artificial neural networks (ANNs) to forecast RTN levels using traffic information data [22]. Traffic data, including audio recordings using a Samsung Galaxy A12 Model SM-A127F/DS Android Smartphone, equivalent noise levels (Leq) using a Lutron SL-4033SD Class 1 Sound Level Meter (SLM), vehicular volume using a manual tally form, and speed using a speed gun, was collected across 42 locations within Nairobi. Using this data, an Artificial Neural Network (ANN), Multi-Layer Perceptron (MLP) model, was developed with two hidden layers. Hyperparameter tuning via grid search was done to optimize model performance. The model achieved a Mean Absolute Error (MAE) of 0.97 dBA and an R2 value of 0.90, outperforming traditional statistical models like CoRTN with an MAE of 5.0 dBA and RLS-90 with an MAE of 11.0 dBA. These results highlight the model’s high accuracy in predicting Nairobi’s RTN. The model’s deployment on a web-based dashboard enables real-time noise monitoring and stakeholder engagement. This pioneering smart predictive model for Nairobi offers a scalable solution for urban noise management [25], with potential applications in traffic planning and policy implementation.
References
[1] World Health Organization, 2018. Environmental Noise Guidelines for the European Region. World Health Organization.
[2] Begam, Nazia & Bashar, Abu. (2020). Auditory effects and consequences of noise pollution in humans: A scoping review. Journal of Family Medicine & Community Health.
[3] Stansfeld SA, and Matheson MP (2003). Noise pollution: non-auditory effects on health. Br Med Bull. 2003; 68:243-57. doi: 10.1093/bmb/ldg033. PMID: 14757721.
[4] Pascale Antonio, Claudio Guarnaccia, Eloísa Macedo, Paulo Fernandes, Ana I. Miranda, Susana Sargento, Margarida C. Coelho, (2023). Road traffic noise monitoring in a Smart City: Sensor and Model-Based approach, Transportation Research Part D: Transport and Environment, Volume 125,2023,103979, ISSN 1361-9209, https://doi.org/10.1016/j.trd.2023.103979.
[5] Ranpise, Ramesh B. and Tandel, Bhaven N.(2022).Urban road traffic noise monitoring, mapping, modelling, and mitigation: A thematic review" Noise Mapping, vol. 9, no. 1, 2022, pp. 48-66. https://doi.org/10.1515/noise-2022-0004
[6] Ziqin Lan, and Ming Cai,(2021).Dynamic traffic noise maps based on noise monitoring and traffic speed data,Transportation Research Part D: Transport and Environment,Volume 94,2021,102796,ISSN 1361-9209,https://doi.org/10.1016/j.trd.2021.102796.
[7] Garg N. and Maji S.( 2016). A retrospective view of noise pollution control policy in India: status, proposed revisions and control measures. Current Science , 10 July 2016, Vol. 111, No. 1 (10 July 2016), pp. 29-38
[8] Narendra, S.; Davar, S.C (2004). Noise pollution-sources, effects and control. J. Hum. Ecol. 2004, 16, 181–
[9] Atilade, A., Coker, J., Albert, I., & Ogede, R. (2020). Evaluation and analysis of traffic noise in Lagos State Polytechnic, Ikorodu, Lagos State, Nigeria. Nigerian Journal of Physics, 28, 179–210.
[10] Clark, S. N., Alli, A. S., Ezzati, M., Brauer, M., Toledano, M. B., Nimo, J., Moses, J. B., Baah, S., Hughes, A., Cavanaugh, A., Agyei-Mensah, S., Owusu, G., Robinson, B., Baumgartner, J., Bennett, J. E., & Arku, R. E. (2022). Spatial modelling and inequalities of environmental noise in Accra, Ghana. Environmental Research, 214(Pt 2), 113932. https://doi.org/10.1016/j.envres.2022.113932
[11] Tétreault LF, Perron S, Smargiassi A. Cardiovascular health, traffic-related air pollution and noise: are associations mutually confounded? A systematic review. Int J Public Health. 2013 Oct;58(5):649-66. doi: 10.1007/s00038-013-0489-7. Epub 2013 Jul 26. PMID: 23887610; PMCID: PMC3907786.
[12] Zhang, Y., Zhang, X., & Wang, Y. (2024). Urban traffic noise prediction using hybrid machine learning models. Journal of Environmental Management, 350, 119678.
[13] Nourani, V., Gökçekus¸, H., Umar, I.K., 2020. Artificial intelligence-based modeling for predicting the road traffic noise. Appl. Acoust. 157, 107000.
[14] Acosta, Ó., Montenegro, C., González Crespo, R., 2024. Road traffic noise prediction model based on artificial neural networks. Heliyon 10, e36484. https://doi.org/10.1016/j.heliyon.2024.e36484.
[15] Sharma, A., Vijay, R., & Sohony, R. A. (2022). Application of machine learning techniques for predicting road traffic noise. Environmental Monitoring and Assessment, 194(3), 189.
[16] Lee, J., Lee, J., & Kim, J. (2023). Deep learning-based traffic noise prediction in urban environments. Applied Acoustics, 201, 109090.
[17] Kumar, P., Nigam, S. P., & Kumar, N. (2021). Vehicular traffic noise modeling using artificial neural network approach. Transportation Research Part D: Transport and Environment, 92, 102716.
[18] Debnath, A., Singh, P. K., & Banerjee, S. (2022). Vehicular traffic noise modelling of urban area—a contouring and artificial neural network-based approach. Environmental Science and Pollution Research, 29(26), 39948–39972.
[19] Umar, I. K., Nourani, V., & Gökçekuş, H. (2023). Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus. Environmental Science and Pollution Research
[20] European Environment Agency, 2020. Environmental Noise in Europe. European Environment Agency.
[21] Li, H., & Zhang, J. (2023). Real-time traffic noise prediction using deep neural networks in smart cities. Sustainable Cities and Society, 89, 104354
[22] Cammarata, G., Cavalieri, S., Fichera, A., 1995. A neural network architecture for noise prediction. Neural Netw. 8, 963–973.
[23] Genaro, N., Torija, A., Ramos-Ridao, A., Requena, I., Ruiz, D.P., Zamorano, M., 2010. Modeling of acoustic properties in the grasslands and noise reduction by hedges. Appl. Acoust. 71, 1065–1074.
[24] Hamad, K., Khalil, M.A., Shanour, A.S., 2017. Modeling and analysis of traffic noise level using artificial neural network. A case study in Menoufia University, Egypt. Egyptian J. Ear Nose Throat Allied Sci. 18, 27–32.
[25] Torija, A.J., Ruiz, D.P., Ramos-Ridao, A.F., 2012. Application of a methodology for categorizing and differentiating urban soundscapes using acoustical descriptors and semantic- differential attributes. J. Acoust. Soc. Am. 131, 3228–3231.
[26] Mansourkhaki, A., Berangi, M., Haghiri, M., Constantinescu, M., 2018. Road traffic noise prediction model based on the traffic composition. J. Acoust. Soc. Am. 143, 3022–3031.
[27] Kumar, K., Parida, M., Katiyar, V.K., 2013. Optimized height of noise barrier for non- urban highway using artificial neural network. Int. J. Environ. Sci. Technol. 11, 719–730.
[28] Cai, Y., Hodgson, S., Blangiardo, M., Gulliver, J., Morley, D., Fecht, D., Vienneau, D., de Hoogh, K., Key, T., Hveem, K., Elliott, P., & Hansell, A. L. (2018). Road traffic noise, air pollution and incident cardiovascular disease: A joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts. Environment international, 114, 191–201. https://doi.org/10.1016/j.envint.2018.02.048
[29] Tashakor, S., Chamani, A., & Moshtaghie, M. (2023). Noise pollution prediction and seasonal comparison in urban parks using a coupled GIS-artificial neural network model. Environmental Monitoring and Assessment, 195(2).
Downloads
Published
Issue
Section
License
Copyright (c) 2025 American Scientific Research Journal for Engineering, Technology, and Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who submit papers with this journal agree to the following terms.
