Browse > Article
http://dx.doi.org/10.7465/jkdi.2012.23.2.247

Estimation methods of fuel consumption using distance traveled: Focused on Monte Carlo method  

Park, Chun-Gun (Department of Mathematics, Kyonggi University)
Soh, Jin-Young (Korea Energy Economics Institute)
Lee, Yung-Seop (Department of Statistics, Dongguk University)
Publication Information
Journal of the Korean Data and Information Science Society / v.23, no.2, 2012 , pp. 247-256 More about this Journal
Abstract
Recently, estimation of greenhouse gas (GHG) emission has continuously emerged as an important global issue. This study compares various statistical methods for estimation of fuel consumption, which is necessary for calculation of GHG emission in road transportation sector. Existing methods have focused on using merely transportation fuel supply or distance traveled for calculation of fuel consumption. Estimates of GHG emission based on fuel supply, however, cannot reflect various vehicle types or model year. This study suggests and compares, from statistical point of view, several methods, which can be applied to estimate fuel consumption of each vehicle, by combining distance traveled and fuel efficiency (mileage), and total fuel consumption of all vehicles. It also suggests practical measures that can reflect vehicle types and model year to suggested methods for future research.
Keywords
Distance traveled; fuel consumption; fuel efficiency; kernel density estimation; Monte Carlo;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Shim, J. (2011). Variable selection in the kernel Cox regression. Journal of the Korean Data & Information Science Society, 22, 795-801.
2 Silverman, B. W. (1986). Density estimation for statistics and data analysis, Chapman and Hall/CRC, London.
3 Venables, W. N. and Ripley, B. D. (2002). Modern applied statistics with S, Springer, New York.
4 권세혁 (2010). 시뮬레이션 실험조건 이상 진단 연구. <한국데이터정보과학회지>, 21, 853-861.
5 김기동, 고현기, 이태정, 김동술 (2011). 배출량 산정방법에 따른 지자체 도로수송부문의 온실가스 배출량 산정 비교. <한국대기환경학회지>, 27, 405-415.
6 국립환경과학원 (2009). <수송부문 온실가스 기후변화대응 시스템 구축(II): 자동차 온실가스 Bottom-up 배출계수 개발>, 국립환경과학원, 인천.
7 에너지경제연구원 (2011). <에너지통계연보>, 에너지경제연구원, 의왕시.
8 최현석, 김태윤 (2010). 엑셀 매크로 기능을 이용한 표본추출에 관한 연구. <한국데이터정보과학회지>, 21, 481-491.
9 Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988). New S language, Chapman and Hall/CRC, London.
10 Bowman, A. W. and A. Azzalini. (1997). Applied smoothing techniques for data analysis, Oxford University Press, New York.
11 IPCC (2006). 2006 IPCC guidelines for national greenhouse gas inventories, IPCC, Switzerland.
12 Martinez, W. L. and Martinez, A. L. (2002). Computational statistics handbook with MATLAB, Chapman & Hall/CRC, New York.
13 Park, C. G. (2011). On statistical properties of some dierence-based error variance estimators in nonparametric regression with a finite sample. Journal of the Korean Data & Information Science Society, 22, 575-587.
14 Scott, D. W. (1992). Multivariate density estimation: Theory, practice and visualization, Wiley, New York.
15 Sheather, S. J. and Jones M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society B, 53, 683-690.
16 Sheather S. J. (2004). Density estimation. Statistical Science, 19, 588-597.   DOI   ScienceOn