• Home
  • Articles & Issues
    • Current
    • All Issues
  • About
    • Aims and Scope
    • Editorial Board
    • Indexing
    • Sources of Financing
  • For Authors
    • Submission
    • Terms of Publication
    • Formatting Guidelines
    • Peer Review Process
    • Article Processing Charges
    • License Agreement
  • Ethics & Policies
    • Publication Ethics
    • Conflict of Interest
    • Open Access Policy
    • Archiving
    • Complaints Policy
    • Privacy Statement
    • Corrections and Retractions
    • Anti-plagiarism Policy
    • Generative AI Policy
  • Search
  • Contacts
en English
  • Українська Українська

The National Transport University Bulletin

  • Submit an article
  • Home
  • Articles & Issues
    • Current
    • All Issues
  • About
    • Aims and Scope
    • Editorial Board
    • Indexing
    • Sources of Financing
  • For Authors
    • Submission
    • Terms of Publication
    • Formatting Guidelines
    • Peer Review Process
    • Article Processing Charges
    • License Agreement
  • Ethics & Policies
    • Publication Ethics
    • Conflict of Interest
    • Open Access Policy
    • Archiving
    • Complaints Policy
    • Privacy Statement
    • Corrections and Retractions
    • Anti-plagiarism Policy
    • Generative AI Policy
  • Search
  • Contacts

Article

  • Read article
  • Download article

Received 19.01.2026

Revised 21.05.2026

Accepted 25.06.2026

Published 06.07.2026

Retrieved from Vol. 30, No. 2, 2026

Pages 83 -92

  • 59 Views

Suggested citation

Lovha, R. (2026). Operational efficiency of cargo vehicles in terms of energy and environmental criteria. The National Transport University Bulletin, 30(2), 83-92. https://doi.org/10.33744/2308-6645-2026-2-30-83-92

Operational efficiency of cargo vehicles in terms of energy and environmental criteria

Roman Lovha*

roman.lovga@gmail.com

Abstract

Rising fuel costs, stricter decarbonisation requirements for freight transport, and the increasing use of telematics monitoring call for a shift from assessing freight vehicles solely based on average fuel consumption to a mode-specific assessment of their performance on specific routes. The purpose of the study was to substantiate and formalise the model of operational efficiency of vehicles of category N3, which combines fuel, environmental, route, regime, and information indicators. The source base was formed from peer-reviewed publications from 2020-2025, selected based on direct relevance to heavy trucks, real route conditions, telematics or on-board data, eco-management, and emissions. System, comparative, and structural and functional analysis, formalisation of indicators, and scenario modelling were applied. The information of the smart tachograph, Global Positioning System-telematics, Controller Area Network/Fleet Management System/On-Board Diagnostics and fuel sensors was differentiated; equations for fuel consumption per 100 km and tonne-kilometre, weight CO₂, idle and Eco-Roll fractions, average acceleration, speed stability, and integral index were proposed. An analysis of the factors showed that the route and the weight of the road train determine the base load, whilst the most readily controllable factors are the duration of idling, the frequency of acceleration, speed stability, the use of coasting, and the carrier’s organisational decisions. Practical operation of the model was demonstrated on a conditional 100-kilometre route of a road train with a gross weight of 36 tonnes with a load of 20 tonnes. Reducing idle speed from 24 to 12 minutes, forming an Eco-Roll share of 6.9% of driving time, and speed stabilisation reduced estimated fuel consumption from 32.4 to 31.1 litres, and direct CO₂ emissions from 85.54 to 82.10 kg; the integral index increased from 0.804 to 0.933. The above scenario is a demonstration scenario and requires further verification based on actual route data. The practical value lies in the ability to use the model to compare trips, identify the causes of cost overruns, and provide recommendations to the driver and dispatcher under specific route and operational conditions

Keywords:

fuel efficiency; route profile; Eco-Roll; smart tachograph; telematics; Controller Area Network; Fleet Management System

References

  1. Abediasl, H., Ansari, A., Hosseini, V., Koch, C.R., & Shahbakhti, M. (2024). Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 238(12), 3779-3793. doi: 10.1177/09544070231185609.
  2. Ashqar, H.I., Obaid, M., Jaber, A., Ashqar, R., Khanfar, N.O., & Elhenawy, M. (2024). Incorporating driving behavior into vehicle fuel consumption prediction: Methodology development and testing. Discover Sustainability, 5, article number 344. doi: 10.1007/s43621-024-00511-z.
  3. Costagliola, M.A., Marchitto, L., Piras, M., & Berra, A. (2025). Effect of performance packages on fuel consumption optimization in heavy-duty diesel vehicles: A real-world fleet monitoring study. Energies, 18(20), article number 5542. doi: 10.3390/en18205542.
  4. Du, K., Shi, Q., Song, J., Chen, D., & Liu, W. (2025). Prediction of truck fuel consumption based on Crossformer-LSTM characteristic distillation. Applied Sciences, 15(1), article number 283. doi: 10.3390/app15010283.
  5. Fang, L., Li, H., Zheng, Y., & Luo, X. (2025). Driving behavior recognition and fuel economy evaluation for heavy-duty vehicles. Research in Transportation Business & Management, 60, article number 101371. doi: 10.1016/j.rtbm.2025.101371.
  6. Gentilucci, G., Accardo, A., & Spessa, E. (2025). Life cycle greenhouse gas emissions of diesel oil and zero-emission trucks: Systematic review of status and perspectives. Transportation Research Interdisciplinary Perspectives, 32, article number 101563. doi: 10.1016/j.trip.2025.101563.
  7. Gonçalves da Silva, G.R., & Lazar, M. (2022). Long hauling eco-driving: Heavy-duty trucks operational modes control with integrated road slope preview. In 2022 European control conference (ECC) (pp. 1752-1758). Piscataway, NJ: IEEE. doi: 10.23919/ECC55457.2022.9837981.
  8. Hamednia, A., Sharma, N., Murgovski, N., & Fredriksson, J. (2022). Computationally efficient algorithm for eco-driving over long look-ahead horizons. IEEE Transactions on Intelligent Transportation Systems, 23(7), 6556-6570. doi: 10.1109/TITS.2021.3058418.
  9. Hong, J., Liu, Y., Li, M., Zhu, J., Tian, M., & Na, X. (2025). Coordinated predictive cruise and coasting control for energy saving in heavy-duty trucks. Advances in Mechanical Engineering, 17(12). doi: 10.1177/16878132251403630.
  10. Huertas, J.I., Serrano-Guevara, O., Díaz-Ramírez, J., Prato, D., & Tabares, L. (2022). Real vehicle fuel consumption in logistic corridors. Applied Energy, 314, article number 118921. doi: 10.1016/j.apenergy.2022.118921.
  11. Jelti, F., & Saadani, R. (2024). Energy efficiency analysis of heavy goods vehicles in road transportation: The case of Morocco. Case Studies on Transport Policy, 17, article number 101260. doi: 10.1016/j.cstp.2024.101260.
  12. Larson, P.D., Parsons, R.V., & Kalluri, D. (2024). Zero-emission heavy-duty, long-haul trucking: Obstacles and opportunities for logistics in North America. Logistics, 8(3), article number 64. doi: 10.3390/logistics8030064.
  13. Liu, A., Fan, P., He, X., Lu, H., Yu, L., & Song, G. (2025). CatBoost-based fuel consumption modeling and explainable analysis for heavy-duty diesel trucks: Impact of engine, driving behavior, and vehicle weight. Energy, 333, article number 137485. doi: 10.1016/j.energy.2025.137485.
  14. Magnino, A., Marocco, P., Saarikoski, A., Ihonen, J., Rautanen, M., & Gandiglio, M. (2024). Total cost of ownership analysis for hydrogen and battery powertrains: A comparative study in Finnish heavy-duty transport. Journal of Energy Storage, 99, article number 113215. doi: 10.1016/j.est.2024.113215.
  15. Mane, A.S., Djordjevic, B., & Ghosh, B. (2021). A data-driven framework for incentivising fuel-efficient driving behaviour in heavy-duty vehicles. Transportation Research Part D: Transport and Environment, 95, article number 102845. doi: 10.1016/j.trd.2021.102845.
  16. Pavlovic, J., Fontaras, G., Broekaert, S., Ciuffo, B., Ktistakis, M., & Grigoratos, T. (2021). How accurately can we measure vehicle fuel consumption in real world operation? Transportation Research Part D: Transport and Environment, 90, article number 102666. doi: 10.1016/j.trd.2020.102666.
  17. Penchev, M., Johnson, K.C., Raju, A.S.K., & Akinci, T.C. (2025). Comprehensive well-to-wheel life cycle assessment of battery electric heavy-duty trucks using real-world data: A case study in Southern California. Vehicles, 7(4), article number 162. doi: 10.3390/vehicles7040162.
  18. Posada-Henao, J.J., Sarmiento-Ordosgoitia, I., & Correa-Espinal, A.A. (2023). Effects of road slope and vehicle weight on truck fuel consumption. Sustainability, 15(1), article number 724. doi: 10.3390/su15010724.
  19. Schoen, A., Byerly, A., Hendrix, B., Bagwe, R.M., dos Santos, E.C., & Miled, Z.B. (2019). A machine learning model for average fuel consumption in heavy vehicles. IEEE Transactions on Vehicular Technology, 68(7), 6343-6351. doi: 10.1109/TVT.2019.2916299.
  20. Serrano-Guevara, Ó.S., Huertas, J.I., & Giraldo, M. (2025). Real energy efficiency of road vehicles. Energies, 18(8), article number 1933. doi: 10.3390/en18081933.
  21. Zhang, Z., Demir, E., Mason, R., & Di Cairano-Gilfedder, C. (2023). Understanding freight drivers’ behavior and the impact on vehicles’ fuel consumption and CO₂e emissions. Operational Research, 23(4), article number 59. doi: 10.1007/s12351-023-00798-2.
Share
Facebook
Twitter
LinkedIn
Email
Telegram
Viber
WhatsApp

https://doi.org/10.33744/2308-6645-2026-2-30-83-92

Address
01010, Ukraine, Kyiv,
1, M. Omelianovycha-Pavlenka Str.


Email
ntu@ntu-bulletin.com

Main information
  • Aims and Scope
  • Indexing
  • Terms of Publication
  • Editorial Board
  • Publication Ethics
Additional information
  • Complaints Policy
  • Peer Review Process
  • Open Access Policy
  • Anti-plagiarism Policy
  • Generative AI Policy
  • Archiving