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Nowadays, many cities all over the world suffer from noise pollution. Noise is an invisible danger that can cause health problems for both people and wildlife. Therefore, it is essential to estimate the environmental noise level and implement corrective measures. There are a number of noise identification techniques, and the choice of the most appropriate technique depends upon the information required and its application. Analyzing audio data requires three key aspects to be considered such as time period, amplitude, and frequency. Based on the above parameters, the source of noise can be identified. This research paper suggests the utilization of artificial intelligence and machine learning algorithms for the traffic noise detection process. Computational methods are the fastest and most innovative way to analyze raw data sets and predict results. Identifying patterns in these methods requires a large amount of data and computing power. Machine learning models can be trained using three types of data: experimental sound libraries, audio datasets purchased from data providers, and data collected by domain experts. In the scope of the study, an experimental dataset was used to train a model that predicts the correct outcomes based on the inputs, using supervised learning. Developing an accurate model requires high-quality data input. However, incorrect data collection can cause noise in feature sets, as can human error or instrument error. Traffic sound events in the real environment do not usually occur in isolation but tend to have a significant overlap with other sound events. A part of this paper is dedicated to the problems that may arise during traffic noise detection, like incorrect data processing and data collection. It also discusses the ways to improve the quality of the input data. The study also states that the field of transport noise detection would greatly benefit from the development of a centralized railway database based on constructive railroad data, and from a centralized database with railway-specific datasets. Based on preliminary results of traffic noise analysis, modernization of the tram lines was proposed to reduce the environmental noise.

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The method of localization of noise level calculation from a rail vehicle in the city of Lviv is investigated and developed. Models of noise load measurement have been adapted, the measured values have been unified and our own solution has been created on the basis of the road surface, the speed of rail transport and the distance from the noise source. According to the research methods, namely: Schall 03 (from Germany), Nordic Train (Scandinavian countries) was carried out compared to the Bland—Altmann schedule, with which we can adapt the studied results. distance from the noise source. As a result, models for predicting noise load measurements were adapted. The results were carried out compared to the Bland-Altmann plot, which helped us with the comparison table. The purpose of the study is to analyze methods for noise level measurements and adapt them into our realities. Based on the known methods of measuring the noise load of railway vehicles, we can assume that there are no correct methods for the cities of Ukraine (especially Lviv).

Introduction. Scientific and technological progress in all industries and in transport is accompanied by the development and widespread introduction of various equipment, machines and vehicles. The growth of capacities of modern equipment, machines, household appliances, and the rapid development of all modes of transport have led to the fact that people at work and in everyday life are constantly exposed to high-intensity noise. The harmful effects of noise can be the result of occupational diseases, increased overall morbidity, reduced efficiency, increased risk of injuries and accidents associated with impaired perception of warning signals, impaired auditory control of technological equipment, and reduced productivity. The whole complex of changes that occur in the human body during prolonged exposure to noise should be considered as a "noise disease".