Latest revision as of 11:52, 16 February 2021

Abstract

Transport energy modeling is a subject of current interest among transport engineers and scientists concerned with problems of sustainable transport. Transport energy planning is not possible without a reasonable knowledge of past and present energy consumption and likely future demands. In this study, three forms of the energy demand equations are developed in order to improve transport energy demand estimation efficiency for future projections based on genetic algorithm (GA) notion. The Genetic Algorithm Transport Energy Demand Estimation (GATEDE) model is developed using population, gross domestic product and vehicle-km. All equations proposed here are linear and non-linear, of which one is linear, second is exponential and third is quadratic. The quadratic form of the GATEDE model provided better-fit solution to the observed data and can be used with a high correlation coefficient for Turkey's future transport energy projections. It is expected that this study will be helpful in developing highly applicable and productive planning for transport energy policies. The GATEDE gives transport energy demand in comparison with the other transport energy demand projections. The GATEDE model plans the sectoral energy demand of Turkey until 2020. (C) 2003 Elsevier Ltd. All rights reserved. C1 Pamukkale Univ, Fac Engn, Dept Civil Engn, PAU Insaat Muh Kinikli, TR-20017 Denizli, Turkey.

Original document

The different versions of the original document can be found in:

• http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/11695

• http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/18310

• http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/4941

• https://api.elsevier.com/content/article/PII:S0301421503002027?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S0301421503002027?httpAccept=text/plain,

http://dx.doi.org/10.1016/s0301-4215(03)00202-7 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://www.sciencedirect.com/science/article/pii/S0301421503002027,

https://ideas.repec.org/a/eee/enepol/v33y2005i1p89-98.html,

https://academic.microsoft.com/#/detail/2077116645