Modernization of Heavy Loaded Tram Radial Effect on Noise and Vibration

Papán, Daniel; Ižvolt, Libor; Dobeš, Peter

doi:10.3390/app12146947

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…It is emphasized that specific conditions at each measurement site and the characteristics of circulating vehicles play a pivotal role in noise levels and should be considered in the analysis [15,17,18,28]. Moreover, considering the extensive coverage of the surface metro network throughout Porto's urban area, the obtained data allow for inferring the noise levels experienced by the population due to this type of traffic noise.…”

Section: Discussionmentioning

confidence: 99%

Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area

Lázaro,

Costa,

Godinho

2024

Applied Sciences

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The growth in the utilization and development of rail transport within urban networks is crucial for transitioning towards a more sustainable form of mobility. However, challenges related to discomfort and noise pollution arising from rail traffic must be addressed and mitigated to foster a harmonious coexistence between residents and trains. This study focuses on analyzing an experimental campaign conducted on the surface metropolitan network of Porto to study and identify the frequency content and pressure levels associated with light rail traffic. The presented experimental campaign holds significant relevance as it comprises various and distinct circulation conditions within the railway network, enabling a comprehensive characterization of railway noise. The collected data indicates a noticeable increase in sound pressure levels as the speed of circulation rises, particularly emphasizing the 1/3 octave band centered around 1000 Hz. The choice of tracks with components having a limited capacity for absorbing acoustic energy leads to a significant rise in noise levels compared to track solutions with elements exhibiting excellent acoustic energy absorption. Furthermore, the study highlights a substantial increase in noise levels (10 dBA) associated with small radius curves, even at low speeds. These findings underscore the importance of considering the track characteristics and geometric features in noise assessment within rail networks. Therefore, the insights gained from this experimental campaign contribute significantly to the understanding and comprehensive characterization of railway noise under diverse circulation conditions within the railway network.

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Section: Discussionmentioning

confidence: 99%

Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area

Lázaro,

Costa,

Godinho

2024

Applied Sciences

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Analysis of artificial intelligence methods for rail transport traffic noise detection

Melnyk¹,

Pytel²,

Orynchak³

et al. 2022

CDS

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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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Modernization of Heavy Loaded Tram Radial Effect on Noise and Vibration

Cited by 2 publications

References 18 publications

Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area

Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area

Analysis of artificial intelligence methods for rail transport traffic noise detection

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