This article discusses the challenges facing public transport in Ukraine in terms of reducing fuel consumption and emissions. The absence or insufficient development of means and methods for monitoring driver behaviour, as well as high staff turnover, create significant difficulties in controlling drivers and vehicles. A conducted study in Lutsk, the administrative center of the Volyn region, analyzed the driving behavior of passenger buses in the city. Results showed that typical driving modes include idling (40%), acceleration (18%), driving at a constant speed (29%), and braking (13%). The study also revealed average accelerations and decelerations, and these results do not meet the requirements of ecological driving. The correlation between driver behavior and these dynamic acceleration and braking characteristics has been established. Possible causes for this phenomenon are discussed in the study. The article proposes the introduction of modern solutions to solve these problems. These solutions are Eco-Driving Assistance Systems (EDAS) or integrated systems, such as FleetControl from TRIONA, which can help learning operating conditions and reduce fuel consumption and emissions. These programmes can also serve as effective monitoring tools for individual drivers and transport companies. This paper describes these applications and reviews the research related to their use and development. In addition, the article highlights the importance of training drivers in eco-driving as a cost-effective method of improving fuel efficiency in transport companies. The paper concludes by emphasising the need for further research to fully understand the complexities of public transport in Ukraine and the potential benefits of introducing innovative technologies for a more sustainable and efficient future for the industry.
1. Kim, K., Park, J., & Lee, J. (2021). Fuel economy improvement of urban buses with development of an eco-drive scoring algorithm using machine learning. Energies, 14(15), 4471. doi: 10.3390/en14154471 (in English).
https://doi.org/10.3390/en14154471
2. Ma, H., Xie, H., & Brown, D. (2017). Eco-driving assistance system for a manual transmission bus based on machine learning. IEEE Transactions on Intelligent Transportation Systems, 19(2), 572-581. doi: 10.1109/TITS.2017.2775633 (in English).
https://doi.org/10.1109/TITS.2017.2775633
3. Xiong, S., Xie, H., & Tong, Q. (2018). The effects of an eco-driving assistance system for a city bus on driving style. IFAC-PapersOnLine, 51(31), 331-336. doi: 10.1016/j.ifacol.2018.10.069 (in English).
https://doi.org/10.1016/j.ifacol.2018.10.069
4. Machine learning algorithm. Retrieved from: https://www.techtarget.com/whatis/definition/machine-learning-algorithm (in English).
5. Xu, N., Li, X., Liu, Q., & Zhao, D. (2021). An overview of eco-driving theory, capability evaluation, and training applications. Sensors, 21(19), 6547. doi: 10.3390/s21196547 (in English).
https://doi.org/10.3390/s21196547
6. ECOdrive Connected. Retrieved from: https://www.ecodrive.eu/en/product/ecodrive-connected (in English).
7. Ping, P., Qin, W., Xu, Y., Miyajima, C., & Takeda, K. (2019). Impact of driver behavior on fuel consumption: Classification, evaluation and prediction using machine learning. IEEE access, 7, 78515-78532. doi: 10.1109/ACCESS.2019.2920489 (in English).
https://doi.org/10.1109/ACCESS.2019.2920489
8. Almeida, J., & Ferreira, J. (2013). BUS public transportation system fuel efficiency patterns. In 2nd International conference on Machine Learning and computer Science (IMLcS'2013), (pp. 1-5). (in English).
9. Akena, R. p'O. (2014). Improving road transport energy efficiency through driver training. PhD thesis. University of Birmingham. Retrieved from: http://etheses.bham.ac.uk/id/eprint/5275 (in English).
10. Sullman, M. J., Dorn, L., & Niemi, P. (2015). Eco-driving training of professional bus drivers - Does it work?. Transportation Research Part C: Emerging Technologies, 58, 749-759. doi: 10.1016/j.trc.2015.04.010 (in English).
https://doi.org/10.1016/j.trc.2015.04.010
11. Ueki, S. and Takada, Y. (2011) Improvement of fuel economy and CO2 reduction of commercial vehicles by the promotion of Eco-Driving Management System (EMS), SAE Technical Paper Series [Preprint]. doi: 10.4271/2011-28-0048. (in English).
https://doi.org/10.4271/2011-28-0048
12. Scania develops fuel-saving driver support system for award-winning long-haul trucks. Retrieved from: https://www.mathworks.com/company/user_stories/scania-develops-fuel-savi... (in English).
13. Fleetcontrol. Retrieved from: https://www.triona.eu/products_services/products/fleetcontrol/ (in English).
14. Eco-drive for electric buses in coach traffic. Retrieved from: https://www.triona.eu/news/2022/eco-drive-for-electric-buses-in-coach-tr... (in English).
15. Sitovskyi, O. P., Dembitskyi, V. M., Mazyliuk, P. V., & Medviediev, I. I. (2018). Otsinka palyvnoi ekonomichnosti miskykh avtobusiv u yizdovomi tsykli pryvedenomu do realnykh umov rukhu [Evaluation fuel economy of city buses in the urbandriving cycle, adjusted to actual traffic conditions]. Suchasni tekhnolohii v mashynobuduvanni ta transporti [Advances in mechanical engineering and transport], 1(10), 112-116. (in Ukrainian).