Data mining with various optimization methods

Road traffic represents the main source of noise in urban environments that is proven to significantly affect human mental and physical health and labour productivity. Thus, in order to control noise sound level in urban areas, it is very important to develop methods for modelling the road traffic noise. As observed in the literature, the models that deal with this issue are mainly based on regression analysis, while other approaches are very rare. In this paper a novel approach for modelling traffic noise that is based on optimization is presented. Four optimization techniques were used in simulation in this work: genetic algorithms, Hooke and Jeeves algorithm, simulated annealing and particle swarm optimization. Two different scenarios are presented in this paper. In the first scenario the optimization methods use the whole measurement dataset to find the most suitable parameters, whereas in the second scenario optimized parameters were found using only some of the measurement data, while the rest of the data was used to evaluate the predictive capabilities of the model. The goodness of the model is evaluated by the coefficient of determination and other statistical parameters, and results show agreement of high extent between measured data and calculated values in both scenarios. In addition, the model was compared with classical statistical model, and superior capabilities of proposed model were demonstrated. The simulations were done using the originally developed user friendly software package.