Modeling of Sound Exposure in Bus Drivers of Tehran Branch by Neural Network Method

Many parameters effect on the noise exposure of bus drivers, which can be noted the bus type, where the engine, fuel type, age of buses and speed. The object of this study is the neural network modeling of noise exposure in Tehran bus transportation drivers.
Material and Noise levels in 90 buses were sampled in three separate sub-sample including (1)30 Ikaroos buses (2)30 Man buses (3)30 Shahab buses, which were selected by simple random sampling. The results of the measurements and parameters of the bus type, age of buses and work duration of the bus drivers was modeled by neural network analysis.
From the investigated parameters, the age of buses had the most importance (0.700) in the noise exposure of bus drivers. After the age of buses, the location of the engine (0.174) and work duration of the drivers (0.125), were importance in the noise exposure of bus drivers, respectively.
Results showed that the age and type of buses were effective factors in driver's noise exposure levels, which was consistent with previous studies in this field.