International Scientific Journal


In this paper, research is done in the influence of different terrain and traffic conditions on road sections on the driver’s driving performances, i.e. on the car energy efficiency and CO2 emission. A methodology aimed at determining to which extent unfavorable traffic and/or terrain conditions on a road section contribute to the driver’s worse driving performances, and also to determine when the driver’s aggressive driving style is responsible for greater fuel consumption and greater CO2 emission is proposed. In order to apply the proposed methodology, a research study was carried out in a cargo transportation company and 12 drives who drove the same vehicle on five different road sections were selected. As many as 284014 of the instances of the data about the defined parameters of the road section and the driver’s driving style were collected, based on which and with the help of machine learning a prediction of the scores for the road section and the scores for the driver’s driving style was performed. The obtained results have shown that the proposed methodology is a useful tool for managers enabling them to simply and quickly determine potential room for increasing the energy efficiency of the vehicle fleet and decreasing CO2 emission.
PAPER REVISED: 2021-11-20
PAPER ACCEPTED: 2021-11-26
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Issue 3, PAGES [2321 - 2333]
  1. Poliak, M., et. al., Identification of costs structure change in road transport companies, Communications - Scientific Letters of the University of Zilina, 21 (2019), 3, pp. 8-12.
  2. Ivković, I. ., et. al., Influence of road and traffic conditions on fuel consumption and fuel cost for different bus technologies, Thermal Science, 21 (2017), 1, pp. 693-706. doi:
  3. Kovács, G., Optimization method and software for fuel cost reduction in case of road transport activity, Acta Polytechnica, 57 (2017), 3, pp. 201-8. doi:
  4. Palander, T., et. al., Comparison of energy efficiency indicators of road transportation for modeling environmental sustainability in "green" circular industry, Sustainability, 12 (2020), 7, pp. 2740. doi:
  5. Stokic, M., et. al., A New Comprehensive Approach for Efficient Road Vehicle Procurement Using Hybrid DANP-TOPSIS Method, Sustainability, 12 (2020), 10, pp. 4044. doi:
  6. Vujanović, D.B., et. al., A hybrid multi-criteria decision making model for the vehicle service center selection with the aim to increase the vehicle fleet energy efficiency, Thermal Science, 22 (2018), 3, pp. 1549-61. doi:
  7. Bakibillah, A.S.M., et. al., Fuzzy-tuned model predictive control for dynamic eco-driving on hilly roads, Applied Soft Computing, 99 (2021), pp. 106875. doi:
  8. Krause, J., et. al., EU road vehicle energy consumption and CO2 emissions by 2050 - Expert-based scenarios, Energy Policy, 138 (2020), pp. 111224. doi:
  9. Oka, S.N., Energy efficiency in Serbia: Research and development activities, in: Sustainable Energy Technologies (Ed. Hanjalić, K. et. al.), Springer, Dordrecht, The Netherlands, 2008, pp. 281-301. doi:
  10. Huang, Y., et. al., Eco-driving technology for sustainable road transport: A review, Renewable and Sustainable Energy Reviews, 93 (2018), pp. 596-609. doi:
  11. Brand, C., et. al., Lifestyle, efficiency and limits: modelling transport energy and emissions using a socio-technical approach, Energy Efficiency, 12 (2019), 1, pp. 187-207. doi:
  12. Yao, Y., et. al., Driving Simulator Study: Eco-Driving Training System Based on Individual Characteristics, Transportation Research Record, 2673 (2019), 8, pp. 463-76. doi:
  13. Schall, D.L., Mohnen, A., Incentivizing energy-efficient behavior at work: An empirical investigation using a natural field experiment on eco-driving, Applied Energy, 185 (2017), pp. 1757-68. doi:
  14. Silva Cruz, I., Katz-Gerro, T., Urban public transport companies and strategies to promote sustainable consumption practices, Journal of Cleaner Production, 123 (2016), pp. 28-33. doi:
  15. Stokic, M., et. al., Evaluation of driver's eco-driving skills based on fuzzy logic model - A realistic example of vehicle operation in real-world conditions, Journal of Applied Engineering Science, 17 (2019), 2, pp. 217-23. doi:
  16. Zdravković, ., et. al., Evaluation of professional driver's eco-driving skills based on type-2 fuzzy logic model, Neural Computing and Applications, 33 (2021), 18, pp. 11541-54. doi:
  17. de Abreu e Silva, J., et. al., Influential vectors in fuel consumption by an urban bus operator: Bus route, driver behavior or vehicle type?, Transportation Research Part D: Transport and Environment, 38 (2015), pp. 94-104. doi:
  18. Boriboonsomsin, K., Barth, M., Impacts of road grade on fuel consumption and carbon dioxide emissions evidenced by use of advanced navigation systems, Transportation Research Record, 2139 (2009), 1, pp. 21-30. doi:
  19. Saboohi, Y., Farzaneh, H., Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption, Applied Energy, 86 (2009), 10, pp. 1925-32. doi:
  20. Smit, R., et. al., Assessing the impacts of ecodriving on fuel consumption and emissions for the Australian situation, Proceedings, ATRF 2010: 33rd Australasian Transport Research Forum, Canberra, Australia, 2010, Vol. 33, pp. 1-15.
  21. Eftekhari, H.R., Ghatee, M., Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition, Transportation Research Part F: Traffic Psychology and Behaviour, 58 (2018), pp. 782-96. doi:
  22. Said, H., et. al., Utilizing Telematics Data to Support Effective Equipment Fleet-Management Decisions: Utilization Rate and Hazard Functions, Journal of Computing in Civil Engineering, 30 (2016), 1, pp. 04014122. doi:
  23. Samuel, A.L., Some Studies in Machine Learning Using the Game of Checkers, IBM Journal, (1959), pp. 210-29.
  24. Peppes, N., et. al., Driving behaviour analysis using machine and deep learning methods for continuous streams of vehicular data, Sensors, 21 (2021), 14, pp. 4704. doi:
  25. Liu, J., et. al., Development of Driver-Behavior Model Based on WOA-RBM Deep Learning Network, Journal of Advanced Transportation, 2020 (2020), pp. 1-11. doi:
  26. Elamrani Abou Elassad, Z., et. al., The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review, Engineering Applications of Artificial Intelligence, 87 (2020), pp. 103312. doi:
  27. Witten, I.H., et. al., Data mining: practical machine learning tools and techniques Fourth Edition, Elsevier, Cambridge, United States, 2017
  28. ***, ACEA - European Automobile Manufacturers Association, WLTP FACTS,
  29. ***, Carbon Dioxide Emissions Intensity for New Australian Light Vehicles, National Transport Commission - NTC, Melbourne, Australia, 2019.

© 2022 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence