한국도로학회논문집 (International Journal of Highway Engineering)

  • 제19권4호
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  • Pages.1-7
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  • 2017
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  • 1738-7159(pISSN)
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  • 2287-3678(eISSN)
한국도로학회 (Korean Society of Road Engineers)
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딥러닝을 이용한 다변량, 비선형, 과분산 모델링의 개선: 자동차 연료소모량 예측

Improvement of Multivariable, Nonlinear, and Overdispersion Modeling with Deep Learning: A Case Study on Prediction of Vehicle Fuel Consumption Rate

  • 투고 : 2017.02.07
  • 심사 : 2017.07.25
  • 발행 : 2017.08.16
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초록

PURPOSES : This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors. METHODS: Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling. RESULTS : The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables. CONCLUSIONS : Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

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참고문헌

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피인용 문헌

  1. Prediction of Asphalt Pavement Service Life using Deep Learning vol.20, pp.2, 2018, https://doi.org/10.7855/IJHE.2018.20.2.057