干旱区生态环境知识资源中心(Arid): Roadway traffic noise modelling in the hot hyper-arid Arabian Gulf region using adaptive neuro-fuzzy interference system

Arid

DOI	10.1016/j.trd.2021.102917
	Roadway traffic noise modelling in the hot hyper-arid Arabian Gulf region using adaptive neuro-fuzzy interference system
	AlKheder, Sharaf; Almutairi, Reyouf
通讯作者	AlKheder, S (corresponding author), Kuwait Univ, Coll Engn & Petr, Civil Engn Dept, SAFAT, POB 5969, Safat 13109, Kuwait.
来源期刊	TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
ISSN	1361-9209
EISSN	1879-2340
出版年	2021
卷号	97
英文摘要	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.
英文关键词	Traffic noise Neuro-fuzzy inference system RMSE Sensitivity analysis
类型	Article
语种	英语
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000703706800008
WOS关键词	ENVIRONMENTAL NOISE ; INFERENCE SYSTEM ; POPULATION ; PREDICTION ; SCHOOLS ; LOGIC
WOS类目	Environmental Studies ; Transportation ; Transportation Science & Technology
WOS研究方向	Environmental Sciences & Ecology ; Transportation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364800
作者单位	[AlKheder, Sharaf; Almutairi, Reyouf] Kuwait Univ, Coll Engn & Petr, Civil Engn Dept, SAFAT, POB 5969, Safat 13109, Kuwait
推荐引用方式 GB/T 7714	AlKheder, Sharaf,Almutairi, Reyouf. Roadway traffic noise modelling in the hot hyper-arid Arabian Gulf region using adaptive neuro-fuzzy interference system[J],2021,97.
APA	AlKheder, Sharaf,&Almutairi, Reyouf.(2021).Roadway traffic noise modelling in the hot hyper-arid Arabian Gulf region using adaptive neuro-fuzzy interference system.TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT,97.
MLA	AlKheder, Sharaf,et al."Roadway traffic noise modelling in the hot hyper-arid Arabian Gulf region using adaptive neuro-fuzzy interference system".TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 97(2021).