Neural Network Modeling of In-Vehicle Noises with Different Roadway Roughness

Qiao, Fengxiang; Li, Q.; Yu, Lei

doi:10.1061/9780784479810.020

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The vibrations measured from a vehicle that are unintentionally generated are considered vehicle interior noise for drivers, which is usually a result of the vibrations from engine operation, vehicle maneuvering, such as speed, acceleration, and braking, and other operational circumstances, such as pavement materials and roughness, wind force, tired materials, weather, traffic flow, ambient vehicles, etc. (Crocker, 2007;Hibbs and Larson 1996;Li et al, 2016a;Liang et al, 2012;Qiao et al, 2016;Sandberg and Ejsmont, 2002;Li et al, 2016b). Previous studies have proven that the vehicle interior noise on freeways could be up to 93 dB(A) and a heavy-duty truck passing can emit 90 dB(A) at 15.24 m (50 ft) distance (Klein, 2003).…”

Section: Introductionmentioning

confidence: 99%

Modeling vehicle interior noise exposure dose on freeways: Considering weaving segment designs and engine operation

Qiao

et al. 2018

Journal of the Air & Waste Management Association

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Vehicle interior noise is usually ignored in the public, and its modeling and evaluation are generally conducted in a laboratory 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. In addition, 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.

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Section: Introductionmentioning

confidence: 99%

Modeling vehicle interior noise exposure dose on freeways: Considering weaving segment designs and engine operation

Qiao

et al. 2018

Journal of the Air & Waste Management Association

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“…Field and in-lab tests are limited to the availabilities of equipment and testing environment/scenarios, while the model estimation may not consider all real conditions and might induce more or less errors [15]. Many studies found that vehicle emissions are very subject to many factors, such as driving behaviors [16], vehicle information [17], pavement materials [18], route's slope conditions [19], traffic control system [20], and traffic situations, such as the situation at a work zone and a signalized intersection [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A K-Nearest Neighbor Model of Light-Duty Vehicle Emission Factors Considering Pavement Roughness

Li¹,

Qiao²,

Yu³

2017

J Civil Environ Eng

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Emission factors are very important measures for developing an emission inventory, making decisions, designing control strategies, mitigating climate change, and even improving public health, in terms of respiratory system diseases. The emission factors could be either measured from field tests or estimated by an emission model. Existing models seldom consider the impacts of some special factors such as pavement roughness. As the impacts of the pavement roughness on emissions are very complicated, a linear model or physical model may not depict the mappings from affecting factors to resulted emission factors. In this paper, two non-linear models, including K-Nearest Neighbor (K-NN) and Neural Network (NN) were built to estimate vehicle emission factors using roughness involved input data. A best fitted model was identified to illustrate the emission pattern along a wide range of pavement roughness. Multiple field tests were conducted in five regions of the State of Texas, United States, with a total of 1,609 km test length. One dedicated test vehicle was employed throughout the test. Pavement roughness was tested using a smartphone based application. All tested data were separated into four groups, each representing a different range of roughness, while the modeling was conducted within each group. The predictive performance of each model was evaluated by (1) correlation coefficient; (2) relative errors; and (3) two tailed unequal variance t-test. Results suggest that, K-NN can be better than NN to model the emission factors for the Texas highway system, and driving on a smoother and rougher pavement result in higher vehicle emissions.

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Neural Network Modeling of In-Vehicle Noises with Different Roadway Roughness

Cited by 2 publications

References 4 publications

Modeling vehicle interior noise exposure dose on freeways: Considering weaving segment designs and engine operation

Modeling vehicle interior noise exposure dose on freeways: Considering weaving segment designs and engine operation

A K-Nearest Neighbor Model of Light-Duty Vehicle Emission Factors Considering Pavement Roughness

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