How To Minimize Transportation Noise Pollutions And Transportation Noise-Related Diseases Using Artificial Intelligence

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.