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Prediction Model of Mass Rapid Transit Noise Level Using the Stepwise Regression Analysis

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dc.contributor.authorHamid N.B.en_US
dc.contributor.authorSanik M.E.en_US
dc.contributor.authorNoor H.M.en_US
dc.contributor.authorPrasetijo J.en_US
dc.contributor.authorMokhtar M.en_US
dc.contributor.authorAzmi M.A.M.en_US
dc.contributor.authorYahaya M.I.en_US
dc.contributor.authorRamli M.Z.en_US
dc.contributor.authorid57190252816en_US
dc.contributor.authorid42561659300en_US
dc.contributor.authorid57807815400en_US
dc.contributor.authorid36562293000en_US
dc.contributor.authorid57190069071en_US
dc.contributor.authorid57221099934en_US
dc.contributor.authorid57712384300en_US
dc.contributor.authorid57195984780en_US
dc.date.accessioned2023-05-29T09:40:16Z
dc.date.available2023-05-29T09:40:16Z
dc.date.issued2022
dc.descriptionAcoustic noise; Noise pollution; Rapid transit; Wind; Mass rapid transit; MRT train; Noise levels; Non-operating hour; Operating hours; Prediction modelling; Regression modelling; Sound level meter; Stepwise regression analysis; Regression analysisen_US
dc.description.abstractNowadays, rail transport is one of the most important transport modes chosen by Malaysians. However, the noise pollution caused by the railway causes complaints from residents living near this track. Therefore, the operator needs to order their workers to conduct monthly observations and measurements of their train noise level in the selected area. The conventional method requires more time and energy as the number of areas to monitor is various and the sound level measurement tools used are also expensive. Thus, a study was carried out to determine the current noise level produced by MRT train in the residential areas near to the Pusat Bandar Damansara station. The noise level measurement was conducted at Lorong Kasah Tepi and Medan Damansara Carpark, which are located nearby the Sungai Buloh�Kajang MRT Line. The noise level was measured at each location with three different slope distances using a sound level meter during operating and non-operating hours. Other than that, MRT speed and wind speed were measured as predictors to develop the Mass Rapid Transit Noise prediction model using the stepwise regression analysis. From the analysis, 88.37% of variation in Mass Rapid Transit Noise can be explained by the regression model. � 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-16-8903-1_33
dc.identifier.epage389
dc.identifier.scopus2-s2.0-85134319357
dc.identifier.spage379
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85134319357&doi=10.1007%2f978-981-16-8903-1_33&partnerID=40&md5=5f19376ea20170ef19399f03319b58f6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27155
dc.identifier.volume273
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleSpringer Proceedings in Physics
dc.titlePrediction Model of Mass Rapid Transit Noise Level Using the Stepwise Regression Analysisen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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