This paper proposes a model based on machine learning for the prediction of road traffic noise for the city of Bogota-Colombia. The input variables of the model were: vehicle capacity, speed, type of flow and number of lanes. The input data were obtained through measurement campaigns in which audio and video recordings were made. The audio recordings, made with a measuring microphone calibrated at a height of 4 meters, made it possible to calculate the noise levels through software processing. On the other hand, by processing the video data, the capacity, and speed of the vehicle were obtained. This process was carried out by means of a classifier trained with images of vehicles taken in the field and free databases. In order to determine the machine learning algorithm to be used, five models were compared, which were configured with their respective hyperparameters obtained through mesh search. The results showed that the Multilayer Perceptron (MLP) regression had the best fit with an MAE of 0.86 dBA for the test data. Finally, the proposed MLP regressor was compared with some classical statistical models used for road traffic noise prediction. The main conclusion is that the MLP regressor obtained the best error and fit indicators with respect to traditional statistical models.
