How To Minimize Transportation Noise Pollutions And Transportation Noise-Related Diseases Using Artificial Intelligence
DOI:
https://doi.org/10.23918/eajse.v11i2p6Keywords:
Artificial Intelligence, Artificial Neural Networks, Machine Learning, Noise Pollution, Environment, Transportation Noise, AI-based techniquesAbstract
Transportation noise pollution has become a global environmental issue with serious health and ecological consequences. This article examines the role of artificial intelligence (AI) in addressing the complex challenges of noise pollution and minimizing its associated health risks using AI-based techniques, like to use the power of machine learning (ML) or Artificial Neural Networks (ANN) in conjunction with metaheuristic algorithms to minimize transportation noise pollution and transportation noise-related diseases.
The article highlights the limitations of traditional noise monitoring and mitigation methods, emphasizing the potential of AI-driven solutions. AI-based techniques such as noise prediction models, real-time mapping, traffic flow optimization, and smart routing, additionally, it summarizes the transportation noise outlined in several guidelines and standards, including the Environmental Noise Directiv,Noise in Europe – EEA 2014,Environmental noise in Europe – EEA 2020,WHO Environmental noise guidelines for the European Region-, Critical noise values in EU (IG Noise),and etc are explored for reducing noise generation across transportation systems. The article also discusses the challenges of data quality, algorithmic biases, and ethical considerations in AI applications. Finally, it explores the future of AI in transportation noise reduction, including opportunities for interdisciplinary research and the integration of emerging technologies such as blockchain and augmented reality. This review underscores the transformative potential of AI in combating transportation noise pollution and protecting public health.
References
[1] Basner, M., & McGuire, S. (2018). WHO environmental noise guidelines for the European region: a systematic review on environmental noise and effects on sleep. International journal of environmental research and public health, 15(3), 519.
[2] Hahad, O., Kuntic, M., Al-Kindi, S., Kuntic, I., Gilan, D., Petrowski, K., Daiber, A., & Münzel, T. (2024). Noise and mental health: evidence, mechanisms, and consequences. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-024-00642-5
[3] Münzel, T., Molitor, M., Kuntic, M., Hahad, O., Röösli, M., Engelmann, N., Basner, M., Daiber, A., & Sørensen, M. (2024). Transportation Noise Pollution and Cardiovascular Health. Circulation Research, 134(9), 1113-1135. https://doi.org/10.1161/circresaha.123.323584
[4] Vienneau, D., Perez, L., Schindler, C., Lieb, C., Sommer, H., Probst-Hensch, N., ... & Röösli, M. (2015). Years of life lost and morbidity cases attributable to transportation noise and air pollution: a comparative health risk assessment for Switzerland in 2010. International journal of hygiene and environmental health, 218(6), 514-521.
[5] Clark, C., & Stansfeld, S. A. (2007). The effect of transportation noise on health and cognitive development: A review of recent evidence. International journal of comparative psychology, 20(2).
[6] Schubert, M., Hegewald, J., Freiberg, A., Starke, K., Augustin, F., Riedel-Heller, S., Zeeb, H., & Seidler, A. (2019). Behavioral and Emotional Disorders and Transportation Noise among Chil dren and Adolescents: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 16(18), 3336. https://doi.org/10.3390/ijerph16183336
[7] Münzel, T., Treede, H., Hahad, O., & Daiber, A. (2023). Too Loud to Handle? Transportation Noise and Cardiovascular Disease. Canadian Journal of Cardiology, 39(9), 1204-1218. https://doi.org/10.1016/j.cjca.2023.02.018
[8] Zaharna, M., & Guilleminault, C. (2010). Sleep, noise and health: Review. Noise and Health, 12(47), 64. https://doi.org/10.4103/1463-1741.63205
[9] Abel, S. M. (1990). The extra-auditory effects of noise and annoyance: an overview of research. The Journal of otolaryngology, 19, 1-13.
[10] Hu, C.-s., & Tkebuchava, T. (2019). E-noise: An increasingly relevant health risk. Journal of Integrative Medicine, 17(5), 311-314. https://doi.org/10.1016/j.joim.2019.07.003
[11] Passchier-Vermeer, W., & Passchier, W. F. (2000). Noise exposure and public health. Environmental Health Perspectives, 108(suppl 1), 123-131. https://doi.org/10.1289/ehp.00108s1123
[12] Goines, L., & Hagler, L. (2007). Noise Pollution: A Modern Plague. Southern Medical Journal, 100(3), 287-294. https://doi.org/10.1097/smj.0b013e3180318be5
[13] Sharma, H., Rawal, Y., & Batra, N. (2021). Noise Pollution. In Basic Concepts in Environmental Biotechnology (pp. 45-53). CRC Press. https://doi.org/10.1201/9781003131427-5
[14] Datta, P. D. M. (2024). NOISE POLLUTION AND HEALTH IN NORTHERN GREATER MUMBAI: CASE OF RN WARD. ShodhKosh: Journal of Visual and Performing Arts, 5(1). https://doi.org/10.29121/shodhkosh.v5.i1.2024.3442
[15] Gandhi, D. K., Raghatate, A., & Pande, S. V. (2019). Noise Pollution, Awareness, Prevention and Control. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3374908
[16] Hemmat, W., Hesam, A. M., & Atifnigar, H. (2023). Exploring Noise Pollution, Causes, Effects, and Mitigation Strategies: A Review Paper. European Journal of Theoretical and Applied Sciences, 1(5), 995-1005. https://doi.org/10.59324/ejtas.2023.1(5).86
[17] Siano, D. (Ed.). (2012). Noise control, reduction and cancellation solutions in engineering. BoD–Books on Demand.
[18] Suhanek, M., & Grubesa, S. (2021). Innovative Approaches to Noise Reduction. In Noise and Environment. IntechOpen. https://doi.org/10.5772/intechopen.93056
[19] Héritier, H., Vienneau, D., Foraster, M., Eze, I. C., Schaffner, E., Thiesse, L., ... & SNC Study Group. (2017). Transportation noise exposure and cardiovascular mortality: a nationwide cohort study from Switzerland. European journal of epidemiology, 32, 307-315.
[20] Héritier, H., Vienneau, D., Foraster, M., Eze, I. C., Schaffner, E., de Hoogh, K., ... & Röösli, M. (2019). A systematic analysis of mutual effects of transportation noise and air pollution exposure on myocardial infarction mortality: a nationwide cohort study in Switzerland. European heart journal, 40(7), 598-603.
[21] Elmenhorst, E.-M., Griefahn, B., Rolny, V., & Basner, M. (2019). Comparing the Effects of Road, Railway, and Aircraft Noise on Sleep: E xposure–Response Relationships from Pooled Data of Three Laboratory St udies. International Journal of Environmental Research and Public Health, 16(6), 1073. https://doi.org/10.3390/ijerph16061073
[22] Brown, A. L., & Van Kamp, I. (2017). WHO environmental noise guidelines for the European region: A systematic review of transport noise interventions and their impacts on health. International journal of environmental research and public health, 14(8), 873.
[23] Brambilla, G. (2001). Measurements techniques for transportation noise. Radiation protection dosimetry, 97(4), 433-436.
[24] Miller, N. P. (2005). Assessing the Noise from US Transportation Systems: Measures and Countermeasures. TR News, (240).
[25] Mehrotra, A., Shukla, S. P., Shukla, A. K., Manar, M. K., Singh, S. K., & Mehrotra, M. (2024). A Comprehensive Review of Auditory and Non-Auditory Effects of Noise o n Human Health. Noise and Health, 26(121), 59-69. https://doi.org/10.4103/nah.nah_124_23
[26] Freitas, E. F., Sousa, E. A., & Silva, C. C. (2021). Transport Noise and Health. In International Encyclopedia of Transportation (pp. 311-319). Elsevier. https://doi.org/10.1016/b978-0-08-102671-7.10731-6
[27] Sørensen, M., Pershagen, G., Thacher, J. D., Lanki, T., Wicki, B., Röösli, M., Vienneau, D., Cantuaria, M. L., Schmidt, J. H., Aasvang, G. M., Al-Kindi, S., Osborne, M. T., Wenzel, P., Sastre, J., Fleming, I., Schulz, R., Hahad, O., Kuntic, M., Zielonka, J., . . . Daiber, A. (2024). Health position paper and redox perspectives - Disease burden by trans portation noise. Redox Biology, 69, 102995. https://doi.org/10.1016/j.redox.2023.102995
[28] Hammer, M. S., Swinburn, T. K., & Neitzel, R. L. (2014). Environmental Noise Pollution in the United States: Developing an Effe ctive Public Health Response. Environmental Health Perspectives, 122(2), 115-119. https://doi.org/10.1289/ehp.1307272
[29] Van Kempen, E. E. M. M., Casas, M., Pershagen, G., & Foraster, M. (2017). Cardiovascular and metabolic effects of environmental noise. http://rivm.openrepository.com/rivm/handle/10029/620941
[30] Clark, C., Sbihi, H., Tamburic, L., Brauer, M., Frank, L. D., & Davies, H. W. (2017). Association of Long-Term Exposure to Transportation Noise and Traffic- Related Air Pollution with the Incidence of Diabetes: A Prospective Co hort Study. Environmental Health Perspectives, 125(8). https://doi.org/10.1289/ehp1279
[31] Van Kempen, E., Casas, M., Pershagen, G., & Foraster, M. (2018). WHO Environmental Noise Guidelines for the European Region: A Systemat ic Review on Environmental Noise and Cardiovascular and Metabolic Effe cts: A Summary. International Journal of Environmental Research and Public Health, 15(2), 379. https://doi.org/10.3390/ijerph15020379
[32] Recio, A., Linares, C., Banegas, J. R., & Díaz, J. (2016). Road traffic noise effects on cardiovascular, respiratory, and metabol ic health: An integrative model of biological mechanisms. Environmental Research, 146, 359-370. https://doi.org/10.1016/j.envres.2015.12.036
[33] Babisch, W. (2011). Cardiovascular effects of noise. Noise and Health, 13(52), 201. https://doi.org/10.4103/1463-1741.80148
[34] Davies, H. W. (2018). Transportation Noise and Cardio-Metabolic Disease. ISEE Conference Abstracts, 2018(1). https://doi.org/10.1289/isesisee.2018.s01.02.23
[35] Hegewald, J., Schubert, M., Freiberg, A., Romero Starke, K., Augustin, F., Riedel-Heller, S. G., Zeeb, H., & Seidler, A. (2020). Traffic Noise and Mental Health: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 17(17), 6175. https://doi.org/10.3390/ijerph17176175
[36] Stansfeld, S. A., Haines, M. M., Burr, M., Berry, B., & Lercher, P. (2000). A review of environmental noise and mental health. Noise and Health, 2(8), 1-8.
[37] Tzivian, L., Winkler, A., Dlugaj, M., Schikowski, T., Vossoughi, M., Fuks, K., Weinmayr, G., & Hoffmann, B. (2015). Effect of long-term outdoor air pollution and noise on cognitive and p sychological functions in adults. International Journal of Hygiene and Environmental Health, 218(1), 1-11. https://doi.org/10.1016/j.ijheh.2014.08.002
[38] Guha, M. (2022). Noise pollution and mental health. Journal of Mental Health, 31(5), 605-606. https://doi.org/10.1080/09638237.2022.2118694
[39] Sørensen, M., Münzel, T., Brink, M., Roswall, N., Wunderli, J. M., & Foraster, M. (2020). Transport, noise, and health. In Advances in Transportation and Health (pp. 105-131). Elsevier. https://doi.org/10.1016/b978-0-12-819136-1.00004-8
[40] Shannon, G., McKenna, M. F., Angeloni, L. M., Crooks, K. R., Fristrup, K. M., Brown, E., Warner, K. A., Nelson, M. D., White, C., Briggs, J., McFarland, S., & Wittemyer, G. (2015). A synthesis of two decades of research documenting the effects of nois e on wildlife. Biological Reviews, 91(4), 982-1005. https://doi.org/10.1111/brv.12207
[41] Siemers, B. M., & Schaub, A. (2010). Hunting at the highway: traffic noise reduces foraging efficiency in a coustic predators. Proceedings of the Royal Society B: Biological Sciences, 278(1712), 1646-1652. https://doi.org/10.1098/rspb.2010.2262
[42] Sordello, R., Ratel, O., Flamerie De Lachapelle, F., Leger, C., Dambry, A., & Vanpeene, S. (2020). Evidence of the impact of noise pollution on biodiversity: a systemati c map. Environmental Evidence, 9(1). https://doi.org/10.1186/s13750-020-00202-y
[43] Mohamed, A.-M. O., Paleologos, E. K., & Howari, F. M. (2021). Noise pollution and its impact on human health and the environment. In Pollution Assessment for Sustainable Practices in Applied Sciences and Engineering (pp. 975-1026). Elsevier. https://doi.org/10.1016/b978-0-12-809582-9.00019-0
[44] Sordello, R., Flamerie De Lachapelle, F., Livoreil, B., & Vanpeene, S. (2019). Evidence of the environmental impact of noise pollution on biodiversit y: a systematic map protocol. Environmental Evidence, 8(1). https://doi.org/10.1186/s13750-019-0146-6
[45] Jombo, G., & Zhang, Y. (2023). Acoustic-Based Machine Condition Monitoring—Methods and Challenges. Eng, 4(1), 47-79. https://doi.org/10.3390/eng4010004
[46] Dove, G., Chen, S., Fries, D., Johnson, V., Mydlarz, C., Bello, J. P., & Nov, O. (2022). From Environmental Monitoring to Mitigation Action: Considerations, Ch allenges, and Opportunities for HCI. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1-31. https://doi.org/10.1145/3555093
[47] Alías, F., & Alsina-Pagès, R. M. (2019). Review of Wireless Acoustic Sensor Networks for Environmental Noise Mo nitoring in Smart Cities. Journal of Sensors, 2019, 1-13. https://doi.org/10.1155/2019/7634860
[48] Kropp, W., Forssén, J., Ögren, M., & Thorsson, P. (2004, August). The failure of traditional traffic noise control for quiet areas. In Proc. Inter-Noise 2004 (and 33rd International Congress and exposition on Noise Control Engineering) (pp. 22-25).
[49] Bello, J., Silva, C., Nov, O., DuBois, R., Arora, A., ... & Doraiswamy, H. (2018). Sonyc: A system for the monitoring analysis and mitigation of urban noise pollution. Communications of the ACM, 5.
[50] Bardoutsos, A., Filios, G., Katsidimas, I., Krousarlis, T., Nikoletseas, S., & Tzamalis, P. (2020/5//). A multidimensional human-centric framework for environmental intellige nce: Air pollution and noise in smart cities.
[51] Ranpise, R. B., & Tandel, B. N. (2022). Urban road traffic noise monitoring, mapping, modelling, and mitigatio n: A thematic review. Noise Mapping, 9(1), 48-66. https://doi.org/10.1515/noise-2022-0004
[52] Syed, M. M., Mohammad, S. K., Mohd, S., & Amandeep, K. (2024). Artificial Intelligence (AI), Machine Learning (ML) & Deep Learning (D L): A Comprehensive Overview on Techniques, Applications and Research Directions. 2024 2nd International Conference on Sustainable Computing and Smart S ystems (ICSCSS). https://doi.org/10.1109/ICSCSS60660.2024.10625198
[53] Khakre, V. B., & Dr, S. K. (2024). A Comprehensive Study on Artificial Intelligence and Machine Learning. International Journal of Advanced Research in Science, Communication a nd Technology. https://doi.org/10.48175/ijarsct-19939
[54] Dubey, A., Yadav, M. U., Kumar, M., & Kumar, J. D. (2024). Navigating the landscape of artificial intelligence, Machine learning and Deep Learning.
[55] Wolfram, B. (2022). Artificial Intelligence: Key Technologies and Opportunities. The Cambridge Handbook of Responsible Artificial Intelligence. https://doi.org/10.1017/9781009207898.003
[56] Mishra, C., & Gupta, D. L. (2016). Deep Machine Learning and Neural Networks: An Overview. International Journal of Hybrid Information Technology, 9(11), 401-414. https://doi.org/10.14257/ijhit.2016.9.11.34
[57] Adefemi, A., Ukpoju, E. A., Adekoya, O., Abatan, A., & Adegbite, A. O. (2023). Artificial intelligence in environmental health and public safety: A comprehensive review of USA strategies. World Journal of Advanced Research and Reviews, 20(3), 1420-1434.
[58] Fan, Z., Yan, Z., & Wen, S. (2023). Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health. Sustainability, 15(18), 13493. https://doi.org/10.3390/su151813493
[59] Bari, L. F., Ahmed, I., Ahamed, R., Zihan, T. A., Sharmin, S., Pranto, A. H., & Islam, M. R. (2023). Potential Use of Artificial Intelligence (AI) in Disaster Risk and Eme rgency Health Management: A Critical Appraisal on Environmental Health. Environmental Health Insights, 17. https://doi.org/10.1177/11786302231217808
[60] Weichenthal, S., Hatzopoulou, M., & Brauer, M. (2019). A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epi demiology. Environment International, 122, 3-10. https://doi.org/10.1016/j.envint.2018.11.042
[61] Konya, A., & Nematzadeh, P. (2024). Recent applications of AI to environmental disciplines: A review. Science of the Total Environment, 906, 167705. https://doi.org/10.1016/j.scitotenv.2023.167705
[62] Miller, T., Cembrowska-Lech, D., Kisiel, A., Krzemińska, A., Kozlovska, P., Jawor, M., Kołodziejczak, M., & Durlik, I. (2023). HARNESSING AI FOR ENVIRONMENTAL RESILIENCE: MITIGATING HEAVY METAL POL LUTION AND ADVANCING SUSTAINABLE PRACTICES IN DIVERSE SPHERES. Grail of Science(26), 151-156. https://doi.org/10.36074/grail-of-science.14.04.2023.027
[63] Tang, K. (2024). Comparative analysis of voice denoising using machine learning and tr aditional denoising. Applied and Computational Engineering, 30(1), 118-124. https://doi.org/10.54254/2755-2721/30/20230083
[64] Lohani, B., Gautam, C. K., Kushwaha, P. K., & Gupta, A. (2024/5/9/). Deep Learning Approaches for Enhanced Audio Quality Through Noise Redu ction.
[65] Rajasekar, A., Ravikumar, B., S, S., G, S., & N, S. A. (2024/10/8/). AI Based Underwater Noise Cancellation to Reduce Ship-induced Marine N oise Pollution.
[66] Ejdfors, K. H. (2023). AI-technology for efficient noise monitoring and analysis in complex u rban soundscapes. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 268(7), 1731-1737. https://doi.org/10.3397/in_2023_0262
[67] Domazetovska Markovska, S., Anachkova, M., Pecioski, D., & Viktor, G. (2024). Advanced concept for noise monitoring in smart cities through wireless sensor units with AI classification technologies. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 270(9), 2779-2790. https://doi.org/10.3397/in_2024_3230
[68] Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., & Wang, J. (2020). Tackling environmental challenges in pollution controls using artifici al intelligence: A review. Science of the Total Environment, 699, 134279. https://doi.org/10.1016/j.scitotenv.2019.134279
[69] Nourani, V., Gökçekuş, H., & Umar, I. K. (2020). Artificial intelligence based ensemble model for prediction of vehicul ar traffic noise. Environmental Research, 180, 108852. https://doi.org/10.1016/j.envres.2019.108852
[70] Umar, I. K., Adamu, M., Mostafa, N., Riaz, M. S., Haruna, S. I., Hamza, M. F., Ahmed, O. S., & Azab, M. (2024). The state-of-the-art in the application of artificial intelligence-bas ed models for traffic noise prediction: a bibliographic overview. Cogent Engineering, 11(1). https://doi.org/10.1080/23311916.2023.2297508
[71] Zambon, G., Angelini, F., Benocci, R., Bisceglie, A., Radaelli, S., Coppi, P., ... & Grecco, R. (2015). DYNAMAP: a new approach to real-time noise mapping. In Proceedings of Euronoise 2015, C. Glorieux, Ed., Maastricht, June 1-3, 2015 (pp. 405-410). DC/ConfOrg.
[72] Qin, Z., & Zhu, Y. (2016, December). Noisesense: A crowd sensing system for urban noise mapping service. In 2016 IEEE 22nd international conference on parallel and distributed systems (ICPADS) (pp. 80-87). IEEE.
[73] Schweizer, I., Bärtl, R., Schulz, A., Probst, F., & Mühläuser, M. (2011, November). NoiseMap-real-time participatory noise maps. In Second international workshop on sensing applications on mobile phones (pp. 1-5). Seattle, WA: Citeseer.
[74] Liu, Y., Ma, X., Shu, L., Yang, Q., Zhang, Y., Huo, Z., & Zhou, Z. (2020). Internet of Things for Noise Mapping in Smart Cities: State of the Art and Future Directions. IEEE Network, 34(4), 112-118. https://doi.org/10.1109/mnet.011.1900634
[75] Lee, G., Moon, S., & Chi, S. (2023). Real-Time Construction Site Noise Mapping System Based on Spatial Inte rpolation. Journal of Management in Engineering, 39(2). https://doi.org/10.1061/jmenea.meeng-5089
[76] Baclet, S., Khoshkhah, K., Pourmoradnasseri, M., Rumpler, R., & Hadachi, A. (2023). Near-real-time dynamic noise mapping and exposure assessment using cal ibrated microscopic traffic simulations. Transportation Research Part D: Transport and Environment, 124, 103922. https://doi.org/10.1016/j.trd.2023.103922
[77] Gunatilaka, D. (2021/12/1/). An IoT-Enabled Acoustic Sensing Platform for Noise Pollution Monitorin g.
[78] Subraja, R., Meyyapan, T., Padmapriya, A., & Kumar, S. S. (2024). Smart noise pollution monitoring system. World Journal of Advanced Engineering Technology and Sciences, 13(1), 899-908. https://doi.org/10.30574/wjaets.2024.13.1.0472
[79] Marques, G., & Pitarma, R. (2019). Noise Monitoring for Enhanced Living Environments Based on Internet of Things. In Advances in Intelligent Systems and Computing (pp. 45-54). Springer International Publishing. https://doi.org/10.1007/978-3-030-16187-3_5
[80] Manglani, T., Srivastava, A., Kumar, A., & Sharma, R. (2022/3/16/). IoT based Air and Sound Pollution Monitoring System for Smart Environm ent.
[81] Rahim, M. A., Rahman, M. M., Rahman, M. A., Muzahid, A. J. M., & Kamarulzaman, S. F. (2021). A Framework of IoT-Enabled Vehicular Noise Intensity Monitoring System for Smart City. In Advances in Intelligent Systems and Computing (pp. 194-205). Springer International Publishing. https://doi.org/10.1007/978-3-030-70917-4_19
[82] Gubbi, J., Marusic, S., Rao, A. S., Yee Wei, L., & Palaniswami, M. (2013/8//). A pilot study of urban noise monitoring architecture using wireless se nsor networks.
[83] Zaheer, R., Ahmad, I., Habibi, D., Islam, K. Y., & Phung, Q. V. (2023). A Survey on Artificial Intelligence-Based Acoustic Source Identificati on. IEEE Access, 11, 60078-60108. https://doi.org/10.1109/access.2023.3283982
[84] Erbe, C., Williams, R., Sandilands, D., & Ashe, E. (2014). Identifying Modeled Ship Noise Hotspots for Marine Mammals of Canada's Pacific Region. PLoS One, 9(3), e89820. https://doi.org/10.1371/journal.pone.0089820
[85] Fernandez, L. P. S., Ruiz, A. R., & Juarez, J. D. J. M. (2010, November). Urban noise permanent monitoring and pattern recognition. In Proceedings of the European Conference of Communications–ECCOM (Vol. 10, pp. 143-148).
[86] Cabell, R., & Fuller, C. (1989/4/10/). A smart pattern recognition system for the automatic identification of aerospace acoustic sources.
[87] Elvas, L. B., Nunes, M., Ferreira, J. C., Francisco, B., & Afonso, J. A. (2023). Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Ph one Data. Applied Sciences, 14(1), 362. https://doi.org/10.3390/app14010362
[88] Brown, A. G. P., Coenen, F. P., Shave, M. J., & Knight, M. W. (1997). An AI Approach to Noise Prediction. Building Acoustics, 4(2), 137-150. https://doi.org/10.1177/1351010x9700400205
[89] Dikshit, S., Atiq, A., Shahid, M., Dwivedi, V., & Thusu, A. (2023). The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas. EAI Endorsed Transactions on Energy Web, 10. https://doi.org/10.4108/ew.4613
[90] Gupta, R., Manna, A., Sharma, H., Pandit, P., & Pan, S. (2024/8/29/). Minimizing Air pollution using Artificial Intelligence.
[91] Lucho, I. R., & Leal, M. L. M. (2024). INTELIGÊNCIA ARTIFICIAL NO TRÂNSITO. Revista ft, 29(140), 30-31. https://doi.org/10.69849/revistaft/ra10202411191030
[92] Nechoska, D. K., Nechkoska, R. P., & Duma, R. (2022/6/16/). Application of Artificial Intelligence for Traffic Data Analysis, Simu lations and Adaptation.
[93] Baha, M. M. (2024). AI-Driven Optimization of Urban Logistics in Smart Cities: Integrating Autonomous Vehicles and IoT for Efficient Delivery Systems. Sustainability. https://doi.org/10.3390/su162411265
[94] Wang, H., Chen, H., & Cai, M. (2018). Evaluation of an urban traffic Noise–Exposed population based on point s of interest and noise maps: The case of Guangzhou. Environmental Pollution, 239, 741-750. https://doi.org/10.1016/j.envpol.2017.11.036
[95] Guerrazzi, E. (2019). Last Mile Logistics in Smart Cities: An IT Platform for Vehicle Sharin g and Routing. In Lecture Notes in Information Systems and Organisation (pp. 251-260). Springer International Publishing. https://doi.org/10.1007/978-3-030-23665-6_18
[96] Venkataraman, S., Baclet, S., & Rumpler, R. (2023). Optimizing noise exposure in the Vehicle Routing Problem: A case study of last-mile freight deliveries in Stockholm. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 265(1), 6138-6149. https://doi.org/10.3397/in_2022_0914
[97] Ranieri, L., Digiesi, S., Silvestri, B., & Roccotelli, M. (2018). A review of last mile logistics innovations in an externalities cost reduction vision. Sustainability, 10(3), 782.
[98] Woschank, M., Rauch, E., & Zsifkovits, H. (2020). A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability, 12(9), 3760.
[99] Amirsalari, B., & Rocha, J. (2023). Recent Advances in Airfoil Self-Noise Passive Reduction. Aerospace, 10(9), 791. https://doi.org/10.3390/aerospace10090791
[100] Vela, A. E., & Oleyaei-Motlagh, Y. (2020/10/11/). Ground Level Aviation Noise Prediction: A Sequence to Sequence Modelin g Approach Using LSTM Recurrent Neural Networks.
[101] Francesco, F., V, V., & Zhiyuan, L. (2020). Roadmaps for AI Integration in the Rail Sector - RAILS. ERCIM News.
[102] Astley, R. J., Agarwal, A., Joseph, P. F., Self, R. H., Smith, M. G., Sugimoto, R., & Tester, B. J. (2007). Predicting and reducing aircraft noise.
[103] Sadeghian, M. (2020). Technologies for aircraft noise reduction: A review. Journal of Aeronautics & Aerospace Engineering.
[104] Seto, E., & Huang, C. H. (2023). The national transportation noise exposure map. MedRxiv, 2023-02.
[105] Marques, G., & Pitarma, R. (2020). A Real-Time Noise Monitoring System Based on Internet of Things for En hanced Acoustic Comfort and Occupational Health. IEEE Access, 8, 139741-139755. https://doi.org/10.1109/access.2020.3012919
[106] Salim, S., Zakir Hussain, I., Kaur, J., & Morita, P. P. (2022/12/17/). Air Pollution Surveillance System: A Big Data Approach to Monitoring A dverse Health Outcomes for Public Health Interventions.
[107] Tinnaluri, V. S. N., Sahukar, L., Chaudhari, A., Malathy, G., Kolavennu, S., & Leonard, L. (2023/5/17/). Cloud and IoT based Framework for Air and Noise Pollution Monitoring S ystem via Extreme Learning Machine.
[108] Neo, E. X., Hasikin, K., Mokhtar, M. I., Lai, K. W., Azizan, M. M., Razak, S. A., & Hizaddin, H. F. (2022). Towards Integrated Air Pollution Monitoring and Health Impact Assessme nt Using Federated Learning: A Systematic Review. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.851553
[109] Kanellopoulos, V., Andrikopoulos, A., & Koutsojannis, C. (2019). A Pilot Study of an Online Intelligent Environmental Observation Syste m for Monitoring and Evaluating Public Health Hazard from Indoor and O utdoor Pollutants. American Journal of Environmental Protection, 8(6), 133. https://doi.org/10.11648/j.ajep.20190806.14
[110] Harris, R. A., Cohn, L. F., & Bowlby, W. (1987). Designing Noise Barriers Using Expert System China. Journal of Transportation Engineering, 113(2), 127-138. https://doi.org/10.1061/(asce)0733-947x(1987)113:2(127)
[111] Kumar, K., Parida, M., & Katiyar, V. K. (2013). Optimized height of noise barrier for non-urban highway using artifici al neural network. International Journal of Environmental Science and Technology, 11(3), 719-730. https://doi.org/10.1007/s13762-013-0257-5
[112] Zannin, P. H. T., Do Nascimento, E. O., Da Paz, E. C., & Do Valle, F. (2018). Application of artificial neural networks for noise barrier optimization. Environments, 5(12), 135.
[113] Starowicz, A., & Zieliński, M. (2024). Sustainable Acoustics: The Impact of AI on Acoustics Design and Noise Management. Technical Sciences. https://doi.org/10.31648/ts.10297
[114] Wickramathilaka, N., Ujang, U., Azri, S., & Choon, T. L. (2022). Influence of urban green spaces on road traffic noise levels:-A review. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 48, 195-201.
[115] Farber, G. S. (2019). Soundprint and the ASA's International Noise Awareness Day Campaign—Re sults, what worked, and going forward. The Journal of the Acoustical Society of America, 145(3_Supplement), 1866-1866. https://doi.org/10.1121/1.5101742
[116] Alsina-Pagès, R. M., Vilella, M., Pons, M., & Garcia Almazan, R. (2019). Mapping the Sound Environment of Andorra and Escaldes-Engordany by Mea ns of a 3D City Model Platform. Urban Science, 3(3), 89. https://doi.org/10.3390/urbansci3030089
[117] Gasco, L., Asensio, C., & de Arcas, G. (2017). Communicating airport noise emission data to the general public. Science of the Total Environment, 586, 836-848. https://doi.org/10.1016/j.scitotenv.2017.02.063
[118] Luzzi, S., Natale, R., & Mariconte, R. (2013). Noise Awareness for smart cities. Proc. ICSV20, Bangok.
[119] Luzzi, S., Natale, R., Carfagni, M., Borchi, F., & Bartalucci, C. (2016). Environmental Noise Directive implementation: state of art, public participation and noise awareness. Noise Theory and Practice, 2(3 (5)), 2-15.
[120] Huang, J., Liu, H., Xi, W., & Kaewunruen, S. (2024). Automated Prognostics and Diagnostics of Railway Tram Noises Using Mac hine Learning. IEEE Access, 12, 183555-183563. https://doi.org/10.1109/access.2024.3512495
[121] Jacuzzi, G., Kuehne, L. M., Harvey, A., Hurley, C., Wilbur, R., Seto, E., & Olden, J. D. (2024). Population health implications of exposure to pervasive military aircr aft noise pollution. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-024-00670-1
[122] Cardoso, L., Rocha, D., & Reis, R. (2023). APLICAÇÃO DO DESIGN GENERATIVO NO DIMENSIONAMENTO DE BARREIRAS ACÚSTIC AS. REEC - Revista Eletrônica de Engenharia Civil, 19(1), 1-23. https://doi.org/10.5216/reec.v19i1.76008
[123] Choi, J., Hong, J., Kang, H., Hong, T., Park, H. S., & Lee, D.-E. (2022). An automatic decision model for optimal noise barrier plan in terms of health impact, productivity, and cost aspects. Building and Environment, 216, 109033. https://doi.org/10.1016/j.buildenv.2022.109033
[124] Poulin, D., & Noel, T. (2023). Automated Noise Recognition and Classification System Based on Artific ial Intelligence. INTER-NOISE and NOISE-CON Congress and Conference Proceedings, 268(8), 26-29. https://doi.org/10.3397/in_2023_0026
[125] Dhiman, N. K., Singh, B., Saini, P. K., & Garg, N. (2020). Design of Optimal Noise Barrier for Metropolitan Cities Using Artifici al Neural Networks. In Lecture Notes on Multidisciplinary Industrial Engineering (pp. 359-375). Springer Singapore. https://doi.org/10.1007/978-981-15-4550-4_22
[126] Attenborough, K. (2023). Noise reduction by greening. Academia Engineering, 1(1).
[127] Chen, T.-Y., Huang, C.-S., & Sung, W.-P. (2025). Improving summer outdoor comfort in metropolitan park: a data-driven a pproach using AI, experimental and design analysis. Journal of Measurements in Engineering. https://doi.org/10.21595/jme.2024.24615
[128] Graziuso, G., Grimaldi, M., Mancini, S., Quartieri, J., & Guarnaccia, C. (2020). Crowdsourcing Data for the Elaboration of Noise Maps: a Methodological Proposal. Journal of Physics: Conference Series, 1603(1), 012030. https://doi.org/10.1088/1742-6596/1603/1/012030
[129] Wisniewski, M., Demartini, G., Malatras, A., & Cudré-Mauroux, P. (2013). NoizCrowd: A Crowd-Based Data Gathering and Management System for Nois e Level Data. In Lecture Notes in Computer Science (pp. 172-186). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-40276-0_14
[130] Guillaume, G., Aumond, P., Bocher, E., Can, A., Écotière, D., Fortin, N., Foy, C., Gauvreau, B., Petit, G., & Picaut, J. (2022). NoiseCapture smartphone application as pedagogical support for educati on and public awareness. The Journal of the Acoustical Society of America, 151(5), 3255-3265. https://doi.org/10.1121/10.0010531
[131] Riegel, K. A., & Resko, J. (2024). Improvements to a mobile app for community noise and assessment data c ollection. The Journal of the Acoustical Society of America, 155(3_Supplement), A300-A300. https://doi.org/10.1121/10.0027577
[132] Williams, W., Zhou, D., Stewart, G., & Knott, P. (2017). Facilitating occupational noise management: The use of a smartphone app as a noise exposure, risk management tool. Journal of Health & Safety Research & Practice, 9(1).
[133] Themann, C. L. (2019). Total hearing health: An approach for raising noise awareness in socie ty. The Journal of the Acoustical Society of America, 145(3_Supplement), 1866-1866. https://doi.org/10.1121/1.5101744
[134] Dubey, R., Bharadwaj, S., & Biswas, D. S. (2020/10/30/). Intelligent Noise Mapping using Smart Phone on Web platform.
[135] Gupta, S., Xu, X., Liu, H., Zhang, J., Bas, J. N., & Kelly, S. K. (2019/12//). Development of Hearing Technology with Personalized Safe Listening Fea tures.
[136] Becker, M., Caminiti, S., Fiorella, D., Francis, L., Gravino, P., Haklay, M., Hotho, A., Loreto, V., Mueller, J., Ricchiuti, F., Servedio, V. D. P., Sîrbu, A., & Tria, F. (2013). Awareness and Learning in Participatory Noise Sensing. PLoS One, 8(12), e81638. https://doi.org/10.1371/journal.pone.0081638
[137] Masood, A., & Ahmad, K. (2021). A review on emerging artificial intelligence (AI) techniques for air pollution forecasting: Fundamentals, application and performance. Journal of Cleaner Production, 322, 129072.
[138] Krupnova, T. G., Rakova, O. V., Bondarenko, K. A., & Tretyakova, V. D. (2022). Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring. Big Data and Cognitive Computing, 6(3), 75. https://doi.org/10.3390/bdcc6030075
[139] Swamynathan, S., Sneha, N., Ramesh, S. P., Niranjana, R., Ponkumar, D. D. N., & Saravanakumar, R. (2024, December). A Machine Learning Approach for Predicting Air Quality Index in Smart Cities. In 2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS) (pp. 1609-1615). IEEE.
[140] Vishi, K. (2023). Privacy and Ethical Considerations of Smart Environments: A Philosophi cal Approach on Smart Meters. In Lecture Notes in Computer Science (pp. 303-313). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-37129-5_25
[141] Giordano, G., Palomba, F., & Ferrucci, F. (2022). On the use of artificial intelligence to deal with privacy in IoT syst ems: A systematic literature review. Journal of Systems and Software, 193, 111475. https://doi.org/10.1016/j.jss.2022.111475
[142] Mukhopadhyay, S. C., Tyagi, S. K. S., Suryadevara, N. K., Piuri, V., Scotti, F., & Zeadally, S. (2021). Artificial Intelligence-Based Sensors for Next Generation IoT Applicat ions: A Review. IEEE Sensors Journal, 21(22), 24920-24932. https://doi.org/10.1109/jsen.2021.3055618
[143] Atlam, H. F., & Wills, G. B. (2019). IoT Security, Privacy, Safety and Ethics. In Internet of Things (pp. 123-149). Springer International Publishing. https://doi.org/10.1007/978-3-030-18732-3_8
[144] Glance, D. G., & Cardell-Oliver, R. (2021). Privacy of Edge Computing and IoT. In Secure Edge Computing (pp. 83-98). CRC Press. https://doi.org/10.1201/9781003028635-7
[145] Usmani, U. A., Happonen, A., & Watada, J. (2023/6/8/). Secure Integration of IoT-Enabled Sensors and Technologies: Engineerin g Applications for Humanitarian Impact.
[146] Popescu, S. M., Mansoor, S., Wani, O. A., Kumar, S. S., Sharma, V., Sharma, A., Arya, V. M., Kirkham, M. B., Hou, D., Bolan, N., & Chung, Y. S. (2024). Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Frontiers in Environmental Science, 12. https://doi.org/10.3389/fenvs.2024.1336088
[147] AboBakr, A., & Azer, M. A. (2017, December). IoT ethics challenges and legal issues. In 2017 12th International Conference on Computer Engineering and Systems (ICCES) (pp. 233-237). IEEE.
[148] Florkowski, W. J. (2008). Opportunities For Innovation Through Interdisciplinary Research.
[149] Resnick, J. C. (2011). Increasing Opportunity through Interdisciplinary Research: Climbing Do wn and Shattering a Tower of Babel. Frontiers in Psychiatry, 2. https://doi.org/10.3389/fpsyt.2011.00020
[150] Earnshaw, R. (2020). Interdisciplinary Research and Development—Opportunities and Challenge s. In Springer Series on Cultural Computing (pp. 373-387). Springer International Publishing. https://doi.org/10.1007/978-3-030-42097-0_20
[151] Gohar, F., Maschmeyer, P., Mfarrej, B., Lemaire, M., Wedderburn, L. R., Roncarolo, M. G., & van Royen-Kerkhof, A. (2019). Driving Medical Innovation Through Interdisciplinarity: Unique Opportu nities and Challenges. Frontiers in Medicine, 6. https://doi.org/10.3389/fmed.2019.00035
[152] Karniouchina, E. V., Victorino, L., & Verma, R. (2006). Product and Service Innovation: Ideas for Future Cross‐Disciplinary Re search*. Journal of Product Innovation Management, 23(3), 274-280. https://doi.org/10.1111/j.1540-5885.2006.00198.x
[153] Domino, S. E., Smith, Y. R., & Johnson, T. R. B. (2007). Opportunities And Challenges of Interdisciplinary Research Career Deve lopment: Implementation of A Women's Health Research Training Program. Journal of Women's Health, 16(2), 256-261. https://doi.org/10.1089/jwh.2006.0129
[154] Wooley, J. C. (2006). Interdisciplinary Innovation in International Initiatives. In Lecture Notes in Computer Science (pp. 14-37). Springer Berlin Heidelberg. https://doi.org/10.1007/11732488_2
[155] Gooch, D., Vasalou, A., & Benton, L. (2016). Impact in interdisciplinary and cross‐sector research: Opportunities a nd challenges. Journal of the Association for Information Science and Technology, 68(2), 378-391. https://doi.org/10.1002/asi.23658
[156] Ekramifard, A., Amintoosi, H., Seno, A. H., Dehghantanha, A., & Parizi, R. M. (2020). A Systematic Literature Review of Integration of Blockchain and Artifi cial Intelligence. In Advances in Information Security (pp. 147-160). Springer International Publishing. https://doi.org/10.1007/978-3-030-38181-3_8
[157] Tian, R., Kong, L., Min, X., & Qu, Y. (2022/5/4/). Blockchain for AI: A Disruptive Integration.
[158] Chavali, B., Khatri, S. K., & Hossain, S. A. (2020/6//). AI and Blockchain Integration.
[159] Goel, V., Kumar, N., Gupta, A. K., & Kumar, S. (2021). Blockchain in AI. In Industry 4.0, AI, and Data Science (pp. 207-229). CRC Press. https://doi.org/10.1201/9781003097181-13
[160] Mane, S. S., & Jadhav, S. S. (2024). Artificial Intelligence in Blockchain Technology. International Journal for Research in Applied Science and Engineering Technology, 12(4), 1185-1190. https://doi.org/10.22214/ijraset.2024.59961
[161] Soliman, M. M., Ahmed, E., Darwish, A., & Hassanien, A. E. (2024). Artificial intelligence powered Metaverse: analysis, challenges and fu ture perspectives. Artificial Intelligence Review, 57(2). https://doi.org/10.1007/s10462-023-10641-x
[162] Lopes, V., & Alexandre, L. A. (2019). An Overview of Blockchain Integration with Robotics and Artificial Int elligence. Ledger. https://doi.org/10.5195/ledger.2019.171
[163] Yoon, S., Leem, J., & Jung, C. (2022). AI Model for Aircraft Noise Identification and Big Data Analysis. Transactions of the Korean Society for Noise and Vibration Engineering, 32(2), 176-185. https://doi.org/10.5050/ksnve.2022.32.2.176 (항공기 소음 식별 및 빅데이터 분석을 위한 AI 모델 연구)
[164] Di Pace, R., Storani, F., Guarnaccia, C., & de Luca, S. (2023). Signal setting design to reduce noise emissions in a connected environ ment. Physica A: Statistical Mechanics and its Applications, 632, 129328. https://doi.org/10.1016/j.physa.2023.129328
[165] Kurra, S., Morimoto, M., & Maekawa, Z. I. (1999). Transportation noise annoyance—a simulated-environment study for road, railway and aircraft noises, Part 1: overall annoyance. Journal of sound and vibration, 220(2), 251-278.
[166] Luca, F., Matteo, B., F, F., & G, L. (2021). Classification of Noise Sources for Port Area Noise Mapping. Environments. https://doi.org/10.3390/ENVIRONMENTS8020012
[167] Munzel, T., Gori, T., Babisch, W., & Basner, M. (2014). Cardiovascular effects of environmental noise exposure. European Heart Journal, 35(13), 829-836. https://doi.org/10.1093/eurheartj/ehu030
[168] Quartieri, J., Mastorakis, N. E., Iannone, G., Guarnaccia, C., D’Ambrosio, S., Troisi, A., & Lenza, T. L. L. (2009, December). A review of traffic noise predictive models. In 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 December (pp. 14-16).
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