Roadway traffic noise modelling in the hot hyper-arid Arabian Gulf region using adaptive neuro-fuzzy interference system

摘要

There is no doubt that traffic noise level is considered a harmful environmental pollution that has a serious impact on human quality of life. This paper shines a light on the traffic noise level in the Arabian Gulf region. More specifically, it predicts the traffic noise level on a ring road in Kuwait by using an adaptive neuro-fuzzy inference system (ANFIS). Field measurements data were collected from 20 different measurement points twice a day. It resulted in 480 measurements of ten variables: traffic noise level, light and heavy vehicle count, average speed of both, road width, building height, pavement condition, and air and roadway temperature. To assist in collecting the data, a vision-based vehicle detection system was developed using machine learning. The system successfully managed to reach an accuracy of 90%, whereas the ANFIS traffic noise prediction model achieved a RMSE of 0.0022. The model was then tested on a different road as a validation step, where it gave a RMSE of 0.06. Afterward, two sensitivity analysis techniques were utilized to rank the nine input variables from the highest relative importance to the lowest: the R-2-based metric and single-input single-output. Based on the results, the most important variable was light vehicle count, and the least effective variable was heavy vehicle count. The air and road temperatures were ranked the fourth and the seventh respectively. Subsequently, four different scenarios were designed to predict the traffic noise level in 2025. The first three scenarios were based on the sensitivity analysis results. Scenario I assumes a reduction in the speed limits on the ring road from 120 km/h to 100 km/h. Scenario II assumes the building height would be high, which will give the same effect as adding a noise barrier. Scenario III assumes there would be a truck curfew in the evening. Finally, Scenario IV assumes there would be no noise control system at all. The results were equal to 76.01, 80.66, 83.36, and 84.56 dBA respectively. It can clearly be seen that a traffic noise control system can reduce traffic noise effectively.