Transport is a key factor influencing greenhouse gas emissions. In relation to this, the issues and challenges facing the transport industry were presented. The issues of challenges for the transport industry related to the European Green Deal were discussed. It discussed how the transport system is critical for European companies and global supply chains. The issues related to the exposure of society to costs are presented: greenhouse gas emissions and pollution. The article deals with the issues of managing transport processes in an enterprise. It was decided to raise the topic of fuel consumption in means of transport. Based on a review of the scientific literature, 3 categories of features are indicated: the vehicle characteristics, the driver's characteristics, and the route's impact on fuel consumption. The study is based on actual data from the archives of the GPS vehicle monitoring system. Data was collected on 1890 routes operated between May 30, 2020, and May 31, 2021. The routes were performed by twenty-nine drivers and 8 vehicles. The vehicles are 40-ton road sets consisting of a tractor unit and a semi-trailer. The analysis of factors influencing fuel consumption is presented. The methodology for conducting feature engineering is described. The benefits of using the method of reducing fuel consumption are presented. The possibilities of using the methods of forecasting electricity and hydrogen consumption in various means of transport, including public transport, where indicated. The data is processed using the Pandas library. The models are compared according to the MAE success measure. The application of methods of working with large data sets is presented. The calculations are made with the help of the NumPy library. Data visualization is done with Matplotlib and Seaborn. Scikit-Learn models are used.
1. Yao, Y., Zhao, X., Liu, C., Rong, J., Zhang, Y., Dong, Z., & Su, Y. (2020). Vehicle fuel consumption prediction method based on driving behavior data collected from smartphones. Journal of Advanced Transportation, 2020. 1-11. doi: 10.1155/2020/9263605 (in English).
https://doi.org/10.1155/2020/9263605
2. Zargarnezhad, S., Dashti, R., & Ahmadi, R. (2019). Predicting vehicle fuel consumption in energy distribution companies using ANNs. Transportation Research Part D: Transport and Environment, 74, 174-188. doi: 10.1016/j.trd.2019.07.020 (in English).
https://doi.org/10.1016/j.trd.2019.07.020
3. Çapraz, A. G., Özel, P., Şevkli, M., & Beyca, Ö. F. (2016). Fuel consumption models applied to automobiles using real-time data: A comparison of statistical models. Procedia Computer Science, 83, 774-781. doi: 10.1016/j.procs.2016.04.166 (in English).
https://doi.org/10.1016/j.procs.2016.04.166
4. Moradi, E., & Miranda-Moreno, L. (2020). Vehicular fuel consumption estimation using real-world measures through cascaded machine learning modeling. Transportation Research Part D: Transport and Environment, 88, 102576. doi: 10.1016/j.trd.2020.102576 (in English).
https://doi.org/10.1016/j.trd.2020.102576
5. Budzyński, A., & Sładkowski A. (2021). The use of machine learning to predict diesel fuel consumption in road vehicles. 19th European Transport Congress of the EPTS Foundation e.V. European Green Deal Challenges and Solutions for Mobility and Logistics in Cities, pp. 207-221 (in English).
6. Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B. E., Bussonnier, M., Frederic, J., & et al. (2016). Jupyter Notebooks-a publishing format for reproducible computational workflows, 2016, 87-90 (in English).
7. McKinney, W. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference, 445(1), pp. 51-56 (in English).
https://doi.org/10.25080/Majora-92bf1922-00a
8. Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: a structure for efficient numerical computation. Computing in science & engineering, 13(2), 22-30. doi: 10.1109/MCSE.2011.37 (in English).
https://doi.org/10.1109/MCSE.2011.37
9. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & et al. (2011). Scikit-learn: Machine learning in Python. The Journal of machine Learning research, 12, 2825-2830 (in English).
10. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in science & engineering, 9(03), 90-95. doi: 10.1109/MCSE.2007.55 (in English).
https://doi.org/10.1109/MCSE.2007.55
11. Waskom, M. L. (2021). Seaborn: statistical data visualization. Journal of Open Source Software, 6(60), 3021. doi: 10.21105/joss.03021 (in English).
https://doi.org/10.21105/joss.03021
12. Dabbish, L., Stuart, C., Tsay, J., & Herbsleb, J. (2012). Social coding in GitHub: transparency and collaboration in an open software repository. In Proceedings of the ACM 2012 conference on computer supported cooperative work, pp. 1277-1286. doi: 10.1145/2145204.2145396 (in English).
https://doi.org/10.1145/2145204.2145396
13. Python 3. Retrieved from: https://docs.python.org/3lastaccessed2022/10/03 (in English).
14. Pandas. Retrieved from: https://pandas.pydata.org/docs/lastaccessed2022/10/03 (in English).
15. NumPy. Retrieved from: https://numpy.org/doc/stable/lastaccessed2022/10/03 (in English).
16. Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., & et al. (2013). API design for machine learning software: experiences from the scikit-learn project. European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, pp. 1-15. (in English).