# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
The results showed that the CNOSOSOS model has a smaller prediction root mean squared error (RMSE) than the NPM model. A typical empirical traffic noise prediction model usually has a very simple mathematical expression with a few parameters. In [13], a nested ensemble filtering (NEF) approach was performed for these parameters' estimation and uncertainty quantification from the empirical model. The NEF approach was compared to the maximum likelihood estimation (MLE) method and outperformed the MLE approach in most conditions. The abovementioned French NMPB2008 and CNOSOSOS-EU models are usually classified as semi-dynamic models, as they both consider proportion noise and rolling noise components separately in relation to traffic speed [14]. Although the CNOSOSOS model is more advanced than most of the empirical models, it is not so easy to implement. A software implementation is needed,and practical guidelines for the input data design to test the real-world situation are limited [15]. A practical implementation of the CNOSSOS model was performed to predict urban noise in [16]. Heavy vehicle volume and velocity data were collected from an automatic monitoring station throughout the entire year of 2013. As a result, the values of sound pressure for heavy vehicles at each center frequency band was calculated with the CNOSSOS method. The physical mechanism of traffic noise is complex in nature. The collected data for traffic noise modelling are of both large scale and high dimension. In this regard, deep learning is a promising method to be applied, as deep learning is powerful for handling huge datasets and modelling nonlinear relations. There have been quite few research performed in the area of environmental noise or traffic noise prediction applying deep learning. In the work of [17], the development of a feedforward ANN model for traffic noise prediction in Sharjah City, United Arab Emirates, was presented. This work validated the noise model performance under different roadway temperatures and found that the temperature was a crucial factor when developing the traffic noise models in hot regions. Although, it was found that the most important features affecting the traffic noise were the distance from the edge of the road and the volume of light vehicles. The authors of [18] applied long short-term memory (LSTM) for environmental noise prediction at different time intervals. The developed model was compared to three classic models such as random walk, stacked autoencoder and support vector machine. The data in this study were collected from urban environmental noise monitoring internet of things (IOT) system. The proposed method outperformed the classical models and showed a promising impact on policy recommendations for the governmental environment noise management. In [19], both sound pressure level (SPL) and loudness level in the near-time future were predicted by applying LSTM deep neural network techniques. The proposed model was validated by comparing with the auto regressive integrated moving average model (ARIMA), for several time periods, ranging from 1 to 60 min. A root mean squared error (RMSE) less than 4.3 dB for SPL and an RMSE less than two phones for loudness were achieved, which outperformed the ARIMA model. The aforementioned two models are based on univariate traffic noise prediction, which means to use the traffic noise from the past to predict the current or future traffic noise. In the work of [20], a conventional ANN model was trained for urban road noise prediction in Tehran. The input features included traffic flow, average speed of the vehicles, vehicle categories, road gradient and surroundings of the test site. The ANN model was validated against the experiment data from the field measurement and compared with some statistical models. A t-test was applied eventually for evaluating the goodness-of-fit of the ANN model. Another work of road traffic noise modelling was also performed in Tehran [21].