In Europe, noise pollution is the second deadliest environmental pollution after air pollution, according to the European Environment Agency [1
]. Exposure to excessive noise levels can cause different health problems and hypertension, leading to reduced sleeping quality. However, the attention of the public to noise pollution is apparently not so much, compared to air quality and water quality concerns. The vehicle manufacturers investigate much more on interior noise reduction than exterior noise, as the customers care more about driving comfort inside the car. Although the exterior noise exposure is not the customers’ major concern, the exposure limit is regulated by governmental authorities. The pass-by noise limit for vehicles with low and medium power engines is already lower than 74 dBA according to United Nation’s Economic Commission for Europe, which will usually decrease after a certain period [2
]. To mitigate the noise pollution, it is necessary to assess the environmental impact of road traffic. Road traffic noise models are especially important, when a measurement campaign is not possible, which costs a lot of time and money [3
]. The road traffic noise model can help us better understand the relations between the traffic noise and traffic features, so that appropriate measures can be taken to mitigate noise pollution, such as speed limit regulation, traffic volume reduction, promotion of electrical vehicles and eco-driving, etc.
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
]. 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 (CNOSSOS-EU) model is one example of semi-dynamic models, although in some literature, the CNOSSOS model is also considered as a statistical model [4
]. 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, CNOSSOS-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 Marion Burgess. 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 CNOSSOS 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 CNOSSOS-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 R2
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
]. The Burgess 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 CNOSSOS 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 CNOSSOS models were compared to the measurement data, collected in 2013 at one hour interval within an entire day. The results showed that the CNOSSOS 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 CNOSSOS-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 CNOSSOS 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
]. 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. The performances of GRU and LSTM are also compared, and the best model is finalized, which is further compared to the CNOSSOS-EU model. Finally, Section 4
concludes this work and proposes some future research.
In conclusion, we are stepping into the new data age, artificial intelligence is under the spotlight across different disciplines, due to its high capability of handling large datasets. In this paper, a recurrent neural network was applied to traffic noise prediction with multivariate traffic features as the predictors. The trained model could predict traffic noise according to the traffic scenario, instead of setting audio devices for traffic noise recording. Simple RNN, LSTM and GRU were introduced and compared in this paper. Different architectures of RNN were trained with the dataset, obtained from an urban roundabout in Blansko, CZ. As a result, the GRU model with many-to-many architecture was selected as the final model, due to its best performance in both prediction accuracy and computation efficiency, with an achieved RMSE of circa 2.4 ± 0.7 dB and a MAE of 2.0 ± 0.5 dB.
The excellent performance of the trained GRU model shows the great potential of applying GRU for traffic noise modelling in the short term. The traffic noise model can help policy makers and urban authorities to carry out different measures, such as speed limit, traffic volume control, new traffic infrastructure assessment, etc., in order to mitigate the noise pollution caused by road traffic. In additional, accurate short-term prediction catches the variations of the traffic noise over a short period, which can further contribute to traffic-noise-peak-caused annoyance studies.
In future research, the obtained GRU model will be generalized. The presented model is trained on the Blansko dataset and applicable in the selected roundabout. However, many other factors, such as road surface, surroundings, and meteorology, were not taken into account in this work, due to the lack of the relevant data. In order to overcome the limitations in obtaining very large datasets, data augmentation techniques such as the generative adversarial network (GAN) could be applied, which was developed by Ian Goodfellow and his colleagues in 2014 [51
]. Additionally, a hybrid model by combining a machine-learning model and empirical or physical noise models could be further developed, in order to take advantage of the strengths from different types of traffic noise models and achieve model generalization. Last but not least, a longer experiment campaign is needed for obtaining more ground truth values and covering wider traffic scenarios and meteorology conditions, for validating the generalized model.