Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment

Muhammad Muhitur Rahman*, Syed Masiur Rahman, Md Shafiullah*, Md Arif Hasan, Uneb Gazder, Abdullah Al Mamun, Umer Mansoor, Mohammad Tamim Kashifi, Omer Reshi, Md Arifuzzaman, Md Kamrul Islam, Fahad S. Al-Ismail

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The road transportation sector in Saudi Arabia has been observing a surging growth of demand trends for the last couple of decades. The main objective of this article is to extract insightful information for the country’s policymakers through a comprehensive investigation of the rising energy trends. In the first phase, it employs econometric analysis to provide the causal relationship between the energy demand of the road transportation sector and different socio-economic elements, including the gross domestic product (GDP), number of registered vehicles, total population, the population in the urban agglomeration, and fuel price. Then, it estimates future energy demand for the sector using two machine-learning models, i.e., artificial neural network (ANN) and support vector regression (SVR). The core features of the future demand model include: (i) removal of the linear trend, (ii) input data projection using a double exponential smoothing technique, and (iii) energy demand prediction using the machine learning models. The findings of the study show that the GDP and urban population have a significant causal relationship with energy demand in the road transportation sector in both the short and long run. The greenhouse gas emissions from the road transportation in Saudi Arabia are directly proportional to energy consumption because the demand is solely met by fossil fuels. Therefore, appropriate policy measures should be taken to reduce energy intensity without compromising the country’s development. In addition, the SVR model outperformed the ANN model in predicting the future energy demand of the sector based on the achieved performance indices. For instance, the correlation coefficients of the SVR and the ANN models were 0.8932 and 0.9925, respectively, for the test datasets. The results show that the SVR is better for predicting energy consumption than the ANN. It is expected that the findings of the study will assist the decision-makers of the country in achieving environmental sustainability goals by initiating appropriate policies.

Original languageEnglish
Article number16064
Issue number23
StatePublished - Dec 2022

Bibliographical note

Funding Information:
The authors acknowledge the support received from the Deanship of Scientific Research at King Faisal University (KFU), King Fahd University of Petroleum & Minerals (KFUPM), and Al-Imam Mohammad Ibn Saud Islamic University, Saudi Arabia, University of Bahrain, and University of Utah, USA.

Funding Information:
This research was funded by the Deanship of Scientific Research at King Faisal University (KFU), Al-Ahsa 31982, Saudi Arabia, through Project No. GRANT1517.

Publisher Copyright:
© 2022 by the authors.


  • Saudi Arabia
  • artificial neural network
  • causality analysis
  • energy demand
  • greenhouse gas emission
  • machine learning
  • road transport
  • support vector regression
  • sustainable environment

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


Dive into the research topics of 'Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment'. Together they form a unique fingerprint.

Cite this