Modelling and evaluation of light railway system’s noise using neural predictors

Background: noise is defined as a sound or series of sounds that are considered to be invasive, irritating, objectionableand disruptive to the quality of daily life. noise is one of the environmental pollutants, and in cities it is usually originatedfrom road traffic, railway traffic, airports, industry etc. the tram is generally considered as environmentally friendly, namelynon-polluting and silent. however complaints from residents living along the tramway lines prove that it may sometimescause annoyance. in this study, a global pointing system (gps) receiver for determining the sampling locations and afrequency based noise measurement system for collecting the noise data are used to analyse the noise level in the citycentre. both environmental (background) and tram noises are measured.results: three types of neural networks are used to predict the noises of the tram and environment. the results of threeapproaches indicate that the proposed neural network with radial basis function (rbf) has superior performance topredict the noises of the tram and environment.conclusions: for making a decision about transportation planning, this network model can help urban planners forevaluating and/or isolating the tram noise in terms of human health.