Author(s): Galatas Alexandros, Dadiotis Konstantinos, Tsoukalas Markos
Over recent years, noise pollution has become a common problem for urban centers and its treatment a major environmental policy challenge. Railway contributes in the overall urban noise environment, mainly through the intermittent transient noise of the surface-line urban networks. In contrast to the more prominent and overlapping road traffic noise, its intermittent nature allows for the source separation and identification from other community noises. In close proximity to the railway corridor, railway transit noise usually supersedes the background level for a couple of seconds, unless it is masked by another random transient noise. With the simultaneous vibration monitoring, false-positive peaks in the noise signal time history can be excluded, as railway transit vibrations are many magnitudes higher than ambient vibrations. \nIn this study, noise & vibration monitoring data was collected from 10,000 train pass-by in various locations in the Athens Tram and Metro Line 1 (surface line) networks. By identifying the railway transient noise, a comparison is made between the daily/hourly average sound level, the average sound level only from the trains and the sound level during train pass-by. The vibration signal is used as a trigger-signal to phase out false-positive peaks in the acoustic signal. By applying machine learning techniques to the collected noise & vibration data, a pattern is investigated that can give reliable results for the railway noise impact even when only the noise signal is used. This pattern can be applied to noise signals at arbitary locations near these railway networks. \nIt is shown that using this method one can assess railway noise and accurately separate the contribution of each train pass-by from other environmental noise sources. Furthermore, it is shown that the use of average indices is not always the appropriate impact assessment method for intermittent transient noises in the modern urban environment.
Name: Mr ALEXANDROS GALATAS