Citation

Li Q, Qiao F, Yu L, Shi J. J. Air Waste Manag. Assoc. (1995) 2018; 68(6): 576-587.

Affiliation

Zhejiang Normal University , No. 688 Yingbin Road, Jinhua City , Zhejiang Province , 321004 , P.R. China.

Copyright

(Copyright © 2018, Air and Waste Management Association, Publisher Informa- Taylor and Francis)

DOI

10.1080/10962247.2017.1350213

PMID

28679084

Abstract

Vehicle interior noise functions at the dominant frequencies of 500 Hz below and around 800 Hz, which fall into the bands that may impair hearing. Recent studies demonstrated that freeway commuters are chronically exposed to vehicle interior noise, bearing the risk of hearing impairment. The interior noise evaluation process is mostly conducted in a lab environment. The test results and the developed noise models may underestimate or ignore the noise effects from dynamic traffic, road conditions and configuration. However, the interior noise is highly associated with vehicle maneuvering. The vehicle maneuvering on a freeway weaving segment is more complex for its nature of conflicting areas. This research is intended to explore the risk of the interior noise exposure on freeway weaving segments for freeway commuters, and improve the interior noise estimation by constructing a decision tree learning based noise exposure dose (NED) model, considering weaving segment designs and engine operation. On-road driving tests were conducted to twelve subjects on State Highway 288 in Houston, Texas. An On-board Diagnosis (OBD) II, a smartphone based roughness app, and a digital sound meter were used to collect vehicle maneuvering and engine information, International Roughness Index, and interior noise levels, respectively. Eleven variables were obtainable from the driving tests, including the length and type of a weaving segment, serving as predictors. The importance of the predictors was estimated by their Out-Of-Bag permuted predictor delta errors. The hazardous exposure level of the interior noise on weaving segments is quantified to Hazard Quotient, NED and daily noise exposure level, respectively.

RESULTS showed that the risk of hearing impairment on freeway is acceptable, the interior noise level is the most sensitive to the pavement roughness, and subject to freeway configuration and traffic conditions. The constructed NED model performs highly predictive power (R = 0.93, NRMSE < 6.7%). IMPLICATION Vehicle interior noise is usually ignored in the public and its modeling and evaluation are generally conducted in a lab environment, regardless of the interior noise effects from dynamic traffic, road conditions, and road configuration. This study quantified the interior exposure dose on freeway weaving segments, which provides freeway commuters with a sense of interior noise exposure risk. Besides, a bagged decision tree based interior noise exposure dose model was constructed, considering vehicle maneuvering, vehicle engine operational information, pavement roughness, and weaving segment configuration. The constructed model could significantly improve the interior noise estimation for road engineers and vehicle manufactures.

Language: en

Keywords

Bagged decision tree model; Freeway weaving segments; Hazard Quotient; Interior noise exposure Feature selection.