چكيده به لاتين
Noise pollution is one of the adverse environmental impacts of the transportation system. In urban areas, on the outskirts of highways and suburban roads, traffic plays a significant role in generating noise pollution. Various indices are used to measure noise pollution. One of the most important indicators is the maximum sound level L10(h). This index depends on various parameters such as traffic volume, average speed, type of vehicles crossing the road, as well as the physical and atmospheric conditions of the route and the surrounding environment. Nowadays, various models are used to estimate this index. Steady-state models estimate the maximum sound level L10(h) as a function of several independent parameters. This function is calibrated by measured data. on the other hand, dynamic models simulate a time-dependent function for estimating sound level. In this thesis, a new steady-state model for predicting L_10 (h) was developed by using a simulated sound level function, which is based on REMELs. To develop and calibrate the proposed model, instead of using field measured data, data from the simulated sound level was used under free-flowing condition. This model estimates L_10 (h) based on traffic volume, average speed, distance, percentage of heavy vehicles, and angles of the road segment. Finally, the new model was validated with the measured data, and the accuracy of the model was verified. To better evaluate the proposed model, the values of L10(h) were also calculated by CoRTN model, and the results were compared with the proposed model and the measured data. The value of mean absolute error for the CoRTN model was 1.71 dB.A, while it was 0.96 dB.A for the proposed model. Also, to further evaluate the proposed model in different traffic conditions, measured data were used in Brazil, and the results showed better performance of the proposed model than the CoRTN model. Since the measured data was not used for calibration, and the new steady-state model was based on the simulated sound level function, the new model has more accuracy and fewer restrictions than other steady-state models.
