An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks

Transport Energy Demand

  • Muhammed Yasin Çodur ERZURUM TECHNICAL UNIVERCITY,ENGINEERING AND ARCHITECTURE FACULTY, CIVIL ENGINEERING DEPARTMENT 25070 ERZURUM
  • Ahmet Ünal
Keywords: artificial neural networks, gross domestic product, transport energy demand, passenger, oil price

Abstract

The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.

Author Biography

Muhammed Yasin Çodur, ERZURUM TECHNICAL UNIVERCITY,ENGINEERING AND ARCHITECTURE FACULTY, CIVIL ENGINEERING DEPARTMENT 25070 ERZURUM

ERZURUM TECHNICAL UNIVERCITY ENGINEERING AND ARCHITECTURE FACULTY,

CIVIL ENGINEERING / TRANSPORTATION DEPARTMENT  Erzurum/TURKEY

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Published
2019-03-26
How to Cite
1.
Çodur MY, Ünal A. An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks. PROMET [Internet]. 2019Mar.26 [cited 2019Oct.18];31(2):151-6. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/3041
Section
Articles