# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
A neural network model to predict hourly sound pressure levels was trained from 50 sampling locations. The trained neural network model was compared to British CoRTN model by hypothesis testing, and the results showed that these two models had no significant difference in the error distribution calculated by the testing dataset. Additionally, in [22], trucks' tire-road noise was studied. Multiple linear regression, ANN and support vector machine (SVM) were applied to train the models, among which both ANN and SVM achieved remarkable results. The obtained models could be very useful for the design and formulation of road pavement and able to provide good advice for road authorities. The authors of [23] collected traffic data for around 3 months in North Cyprus including 94,824 samples, consisting of noise level, traffic volume, vehicle composition, speed and number of horns every 15 min. A nonlinear sensitivity analysis using neural networks was performed to select the most relevant features. Number of cars was proven to be the most important feature by sensitivity analysis. Different machine-learning models, such as support vector regression (SVR), multiple linear regression, feedforward neural networks and adaptive neuro fuzzy inference system (ANFIS), were trained separately on the selected features and noise level. The results of trained models were combined by linear and nonlinear ensemble techniques for the final noise prediction. It was found that the nonlinear ensemble techniques produced the best result, which improved the performance of single models with a great robustness. Apart from traffic noise level prediction, deep learning has also been applied to other traffic-related studies, such as traffic flow state estimation [24], traffic light detection and classification [25], traffic condition forecasting [26], autonomous driving [27] and traffic noise annoyance assessment, related to physical characteristics of sound and subjective perception of the person [28], etc. In the work of [29], a classification model was developed to distinguish road traffic noise and anomalous noise events. The data were collected from wireless acoustic sensor networks in the framework of smart city and IOT. The goal was to detect and remove the anomalous noise events, in order to compute the noise map of urban and suburban in real time. The binary-based Gaussian mixture models were selected as the best core binary classifier, due to the low computational cost and high classification accuracy. In this paper, multivariate time series forecasting for traffic noise prediction is performed, applying recurrent neural network (RNN). The traffic features, such as the traffic volume, vehicle types, vehicle distances to receiver, vehicle speeds and accelerations/decelerations, are the input variables of the model, while the corresponding traffic noise is the output variable. Gated recurrent unit (GRU) is compared to LSTM in both prediction accuracy and computation consumption. Different architectures of recurrent neural networks are for the first time extensively elaborated altogether in one paper. The goal of this paper is to identify the best machine-learning model that is capable of predicting traffic noise in the short term using traffic features. The capability of short-term prediction allows us to track the variations of traffic noise level over short time. This is extremely useful for the studies of traffic annoyance, caused by noise peaks, which cannot be fulfilled by empirical traffic noise models, as their predictions are usually averaged over a long time period [4]. This paper is structured as follows: Section 2 describes the methodology of this study, summarizing the theory of simple RNN, LSTM and GRU. Different RNN architectures and model evaluation metrics are also introduced. Moreover, the experimental setup, obtained traffic video and audio data and raw data pre-processing are all explained in this section. Section 3 demonstrates the trained models and compares the obtained results based on different RNN architectures.