# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network  
[PERSON]  
1 Center for Low-Emission Transport, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria; [PERSON]@uit.ac.at1 Center for Low-Emission Transport, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria; [PERSON]@uit. To model the physical mechanisms of traffic noise by analytical correlations and numeric simulations is rather complex, due to the complicated nature phenomenon and nonlinear processes. Current popular traffic noise models can be mainly classified into three types: empirical models, semi-dynamical models and machine-learning models. Empirical models, also called statistical models in some literature, describe the traffic noise as a function of the traffic volume, vehicle type and sometimes also the average speed of the traffic over a long period of time [4; 5]. Some common empirical traffic noise models include the German RLS 90 model, American federal highway administration (FHWA) model, British calculation of road traffic noise (CoRTN) model, etc. In contrast to empirical models, dynamic models predict the noise level of the traffic at each time step, typically 1 s, based on the instantaneous vehicle speeds and accelerations [4]. Obviously semi-dynamic models are the models in the transition phase from empirical models to dynamic models [5]. The European-Commission-developed common noise assessment methods (CNOSOS-EU) model is one example of semi-dynamic models, although in some literature, the CNOSOS model is also considered as a statistical model [4; 5]. With regards to machine-learning models, they have been emerging over the last years. Machine-learning models try to learn a relationship between traffic noise and traffic features with applying machine-learning technologies. More details with regards to these three categories of traffic noise models will be introduced below. In [5], the performances of the Burgess model, CNOSOS-EU model and Quartieri model were compared pairwise to noise measurements data collected from different locations and traffic conditions. The Burgess model is an empirical model, implemented by [PERSON]. It predicts the traffic noise simply according to the hourly flow of vehicles, distance between the carriage and the receiver and heavy vehicle proportions. The CNOSOSOS model introduces the mean speed of the traffic flow, calculating the rolling noise and propulsion noise separately, with additional corrections for studded tires, air temperature, road gradients and vehicle accelerations. The Quartieri model is presented in a similar way to the Burgess model with a simple function but takes also the mean speed as one of the input parameters. The authors concluded that CNOSOS-EU model and Quartieri model outperform the statistical model of Burgess model. In [6], a statistical road traffic noise model was developed in Iran, with collected data from Hamadan city, applying regression analysis. The dataset consisted of 282 samples and the regression model achieved an \(R^{2}\) of 0.913. The proposed model was further compared to other classical empirical models such as the FHWA model. Several other works performed similar comparisons on different empirical or semi-dynamical traffic noise models against experiment data in [7; 8; 9; 10]. The [PERSON] model, Griffith and Langdon, French CSTB model, Italian C.N.R model, French NMPB-Routes model and German RLS 90 model were explained with mathematic expressions. The authors of [11] discussed the road traffic noise in urban areas in the aspects of economy, society, law and regulation. The regional traffic noise models were briefly introduced, such as UK CoRTN model, US FHWA model, European Harmonoise/IMAGINE model and CNOSOS model. The French NMPB 2008 is recommended as the reference model in forecasting urban traffic noise, by European Directive 2002/49/EC. In [12], the Nordic Prediction Method (NPM) and CNOSOSOS models were compared to the measurement data, collected in 2013 at one hour interval within an entire day.