Developing Machine Learning Models to Predict Roadway Traffic Noise: An Opportunity to Escape Conventional Techniques

Abstract

Accurate prediction of roadway traffic noise remains challenging. Many researchers continue to improve the performance of their models by either adding more variables or improving their modeling algorithms. In this research, machine learning (ML) modeling techniques were developed to predict roadway traffic noise accurately. The ML techniques applied were: regression decision trees, support vector machine, ensembles, and artificial neural network. The parameters of each of these models were fine-tuned to achieve the best performance results. In addition, a state-of-the-art hybrid feature-selection technique has been employed to select a minimum set of input features (variables) while maintaining the accuracy of the developed models. By optimizing the number of features used in the model, the resources needed to develop and utilize a model to predict roadway noise would be less, hence decreasing the development cost. The proposed approach has been applied to develop a free-field roadway traffic noise model for Sharjah City in the United Arab Emirates. The best developed ML model was compared with a conventional regression model which was developed earlier under the same conditions. The cross-validated results clearly indicate that the best ML model outperformed the regression modeling. The performance of the ML model was also assessed after reducing the number of its input features based on the outcome of the feature-selection algorithm; the model performance was slightly affected. This result emphasizes the importance of considering only features that greatly influence the roadway traffic noise.