# Traffic Noise Prediction Applying Multivariate Bi-Directional Recurrent Neural Network
## 4 Conclusions  
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 \(\pm\) 0.7 dB and a MAE of 2.0 \(\pm\) 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 [PERSON] 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. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Testing Data** & **RMSE** & **MAE** & **MAPE** \\ \hline Mic3 & 6.357 & 4.902 & 0.063 \\ Mic5 & 7.706 & 6.294 & 0.079 \\ Mic6 & 7.494 & 6.121 & 0.077 \\ Mic7 & 7.189 & 5.659 & 0.072 \\ Mic8 & 6.967 & 5.539 & 0.071 \\ \hline Average & 7.143 & 5.703 & 0.072 \\ Standard dev. & 0.467 & 0.489 & 0.006 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The evaluations on the common noise assessment methods (CNOSSOS) model. **Author Contributions:** The conceptualization of the study was done by all authors. The writing of the original draft and preparation of the paper was done by [PERSON] All authors contributed to the writing review and final editing. Experiment for data collection was performed by [PERSON] and H.K. Formal analysis, investigation, software and visualizations were done by [PERSON] The supervision was done by H.K. and W.D.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 721615. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. **Acknowledgments:** The author is very grateful to the colleague, [PERSON], for providing the recording device: Head acoustics SQuadriga II.