DOI: https://doi.org/10.32515/2664-262X.2022.6(37).1.173-182

Machine Learning Algorithmic Models for Forecasting Fuel Consumption by Vehicles of the Grain Crops Delivery

Viktoriia Kotenko

About the Authors

Viktoriia Kotenko, post-graduate, Vinnytsya National Technical University, Vinnytsya, Ukraine, e-mail: mialkovska.viktoria@gmail.com, ORCID ID: 0000-0002-0033-3302

Abstract

The analysis of the state of development and use of machine learning algorithmic models in road transport logistics systems has been carried out. The expediency of application of machine learning algorithmic model for predicting fuel consumption by vehicles during the grain crops delivery from agricultural enterprises to the grain elevator has been substantiated. The reggression machine learning algorithmic models: DT (Decision Tree) model and the RF (Random Forest) model for forecasting fuel consumption by vehicles is selected. On the basis of historical data of the enterprise that transports grain crops from agricultural enterprises to the elevator, forecasting of fuel consumption by vehicles with the use proposed models has been carried out. The resulting prediction of vehicle fuel consumption with the use the RF random forest model, as opposed to the values of individual decision trees, has a lower ability to retraining and greater flexibility to the limit of vehicle fuel consumption decisions. Evaluation of the specified models for forecasting fuel consumption by vehicles during the grain crops delivery from agricultural enterprises to the grain elevator has been performed according the following criteria: mean absolute error (MAE), root mean square error (RMSE), Total time and Training time. It has been determined that the best prediction of fuel consumption by vehicles during the grain crops delivery from agricultural enterprises to the grain elevator is performed by the RF random forest model, which provides a relative error of the obtained results of 4.6% with a standard deviation of ±0.1 and a total machine learning time of 4.8s. The obtained results of the researches can be used for the selection of the most efficient means of transport for executing orders of the grain crops delivery from agricultural enterprises to the elevator.

Keywords

machine learning model, fuel consumption, random forest model, decision tree model, grain crops transportation

Full Text:

PDF

References

1. Mhaskar, H.N., & Poggio, T. (2016) Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications. Vol.14, Issue 6, pp. 829–848 [in English].

2. He, K.M., Zhang, X.Y., Ren, S.Q. & Sun J. (2016). Deep Residual Learning for Image Recognition. Proc. Conference on Computer Vision and Pattern Recognition, 770–778 [in English].

3. Kotenko, V. (2021). Development of the grain crops supply chain model. Visnyk mashynobuduvannia ta transportu. 14(2), pp. 33-37. [in English].

4. Tsolaki, K., Vafeiadis, T., Nizamis, A., Ioannidis, D. & Tzovaras, D. (2022). Utilizing machine learning on freight transportation and logistics applications: A review. ICT Express. DOI:10.1016/j.icte.2022.02.001.

5. Samimi, A., Razi-Ardakani, H., Nohekhan, A. (2017). A Comparison between Different Data Mining Algorithms in Freight Mode Choice. American Journal of Applied Sciences 14 (2), 204-216. DOI: 10.3844/ajassp.2017.204.216 [in English].

6. Abdelwahab, W., & Sayed, T. (1999). Freight mode choice models using artificial neural networks. Civil Engineering and Environmental Systems, 16(4), 267-286. DOI:10.1080/02630259908970267 [in English].

7. Tortum, A., Yayla, N. & Gökdağ M. (2009). The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system. Expert Systems with Applications, 36, 6199-6217. DOI:10.1016/j.eswa.2008.07.032 [in English].

8. Van der Spoel, S., Chintan, A., Van Hillegersberg, J. (2015). Predictive analytics for truck arrival time estimation: a field study at a European distribution center, International Journal of Production Research. DOI: 10.1080/00207543.2015.1064183 [in English].

9. Servos, N., Liu, X., Teucke, M. & Freitag, M. (2020). Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms. Logistics 4, 1. DOI:10.3390/logistics4010001 [in English].

10. Yakushenko, A., Shevchuk, D. & Medynskyi, D. (2021). Neiromerezheva model dlia prohnozuvannia chasu na vykonannia transportnoi zadachi. [Neural network model for predicting the execution time of a transport task] Science-Based Technologies, 49(1), 33-38. [in Ukrainian]. DOI:10.18372/2310-5461.49.15289 [in English].

11. Usman, A., & Roorda, MJ. (2022). Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods. Transportation Research Record. 2676(2), 541-552. DOI:10.1177/03611981211044462 [in English].

12. Schoen, A., Byerly, A., Hendrix, B., Bagwe, R. M., Santos, E. C. d., Miled, Z. B. (2019). A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles. IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6343-6351. DOI: 10.1109/TVT.2019.2916299. [in English].

13. Bousonville, T., Cheubou Kamga D., Krüger T., Dirichs, M. (2020). Data driven analysis and forecasting of medium and heavy truck fuel consumption. Enterprise Information Systems. DOI: 10.1080/17517575.2020.1856417 [in English].

14. Topi´c, J., Škugor, B.& Deur, J. (2022). Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data. Sustainability 14, 744. DOI:10.3390/su14020744 [in English].

15. Singh, A., Das, A., Bera, U. K., Lee, G. M. (2021). Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks. IEEE Access, vol. 9, 103497-103512, DOI: 10.1109/ACCESS.2021.3098657. [in English].

16. Bashynsky, O., Medvediev, Ye., Slobodian, S., Skorobogatov, D. (2019). Justification of models of changing project environment for harvesting grain, oilseed and legume crops. Independent Journal of Management & Production (Special Edition PDATU), Vol 10, No 7, pp. 658-672. [in English].

17. Ratushnyi, R.T., Tryhuba, A.M., Khmel, P., Smotr, O.O. & Prydatko, O.V. (2019). Osoblyvosti proektno-oriientovanoho upravlinnia diialnistiu transkordonnykh operatyvno-riatuvalnykh pidrozdiliv. [Peculiarities of project-oriented management of activities of cross-border operative rescue units]. Visnyk LDU BZhD : zb. nauk. prats. Lviv: LDUBZhD,. №19. 51–60. [in Ukrainian].

18. Koval, N., Tryhuba, A., Kondysiuk, I., Tryhuba, I., Boiarchuk, O., Rudynets M. et al. (2021). Forecasting the fund of time for performance of works in hybrid projects using machine training technologies. 3rd International Workshop on Modern Machine Learning Technologies and Data Science Workshop, MoMLeT and DS 2021, CEUR Workshop Proceedings 2917, Lviv-Shatsk, pp. 196–206. [in English].

19. Singh, S., & Gupta, P. (2014). Comparative study ID3, CART and C4.5 Decision tree algorithm: A Survey. International Journal Of Advanced Information Science And Technology (IJAIST), 27(2319:2682). [in English].

20. Lewis, Roger. (2000). An Introduction to Classification and Regression Tree (CART) Analysis. [in English].

21. Daniya, T., Geetha, M., Suresh Kumar, K. (2020). Classification and regression trees with gini index. Advances in Mathematics Scientific Journal. 9. 1857-8438. DOI:10.37418/amsj.9.10.53. [in English].

22. Bauer, E., & Kohavi, R. (1999). An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36, 105–139. DOI:10.1023/A:1007515423169 [in English].

Citations

  1. Mhaskar H.N., Poggio T. Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications. Vol.14. Issue 6. 2016. P. 829–848.
  2. Deep Residual Learning for Image Recognition / He K.M., Zhang X.Y., Ren S.Q., Sun J. Proc. Conference on Computer Vision and Pattern Recognition. 2016. 770–778.
  3. Kotenko V. Development of the grain crops supply chain model. Вісник машинобудування та транспорту. 2021.14(2). C. 33-37.
  4. Utilizing machine learning on freight transportation and logistics applications: A review / Tsolaki K., Vafeiadis T., Nizamis A., Ioannidis D., Tzovaras D. ICT Express. 2022. DOI:10.1016/j.icte.2022.02.001.
  5. Samimi A., Razi-Ardakani H., Nohekhan A. A Comparison between Different Data Mining Algorithms in Freight Mode Choice. American Journal of Applied Sciences. 2017. 14 (2). pp. 204-216. DOI: 10.3844/ajassp.2017.204.216
  6. Abdelwahab W., Saye, T. Freight mode choice models using artificial neural networks. Civil Engineering and Environmental Systems. 1999. 16(4). Pp. 267-286. DOI:10.1080/02630259908970267
  7. Tortum, A., Yayla, N., Gökdağ M. The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system. Expert Systems with Applications. 2009. 36. Pp. 6199-6217. DOI:10.1016/j.eswa.2008.07.032.
  8. Van der Spoel S., Chintan A., Van Hillegersberg J. Predictive analytics for truck arrival time estimation: a field study at a European distribution center. International Journal of Production Research. 2015. DOI: 10.1080/00207543.2015.1064183
  9. Travel Time Prediction in a Multimodal Freight Transport / Servos N., Liu X., Teucke M., Freitag M. Relation Using Machine Learning Algorithms. Logistics. 2020.4. 1. DOI:10.3390/logistics4010001
  10. Якушенко О. С., Шевчук Д. О., Мединський Д. В. Нейромережева модель для прогнозування часу на виконання транспортної задачі. Наукоємні технології. 2021. 49(1). С. 33-38. DOI:10.18372/2310-5461.49.15289
  11. Unmad A., Roorda M. J. Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods. Transportation Research Record. 2022. 2676(2). Pp. 541-552. DOI:10.1177/03611981211044462
  12. A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles / Schoen A., Byerly A., Hendrix B., Bagwe R. M., Santos E. C. d., Miled Z. B. IEEE Transactions on Vehicular Technology. 2019. vol. 68. no. 7. pp. 6343-6351. DOI: 10.1109/TVT.2019.2916299.
  13. Data driven analysis and forecasting of medium and heavy truck fuel consumption / Bousonville T., Cheubou Kamga D., Krüger T., Dirichs M. Enterprise Information Systems. 2020. DOI: 10.1080/17517575.2020.1856417
  14. Topi´c J., Škugor B., Deur J. Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data. Sustainability. 2022. 14. 744. DOI:10.3390/su14020744
  15. Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks / Singh A., Das A., Bera U. K., Lee G. M. IEEE Access. 2021. vol. 9. pp.103497-103512. DOI: 10.1109/ACCESS.2021.3098657.
  16. Bashynsky O., Medvediev Ye., Slobodian S., Skorobogatov D. Justification of models of changing project environment for harvesting grain, oilseed and legume crops. Independent Journal of Management & Production (Special Edition PDATU). 2019. Vol 10. No 7. pp. 658-672.
  17. Особливості проектно-орієнтованого управління діяльністю транскордонних оперативно-рятувальних підрозділів / Ратушний Р. Т. та ін. Вісник ЛДУ БЖД : зб. наук. праць. Львів: ЛДУБЖД, 2019. №19. С. 51–60.
  18. Koval N., Tryhuba A., Kondysiuk I., Tryhuba I., Boiarchuk O., Rudynets M., Grabovets V., Onyshchuk V. Forecasting the fund of time for performance of works in hybrid projects using machine training technologies. 3rd International Workshop on Modern Machine Learning Technologies and Data Science Workshop, MoMLeT and DS 2021, CEUR Workshop Proceedings 2917, Lviv-Shatsk, 2021. pp. 196–206.
  19. Singh, S., & Gupta, P. (2014). Comparative study ID3, CART and C4.5 Decision tree algorithm: A Survey. International Journal Of Advanced Information Science And Technology (IJAIST). 2014. no. 27. Pp. 2319-2682.
  20. Lewis Roger. An Introduction to Classification and Regression Tree (CART) Analysis. 2000.
  21. Daniya. T., Geetha M., Suresh Kumar K. Classification and regression trees with gini index. Advances in Mathematics Scientific Journal. 2020. no. 9. pp.1857-8438. DOI:10.37418/amsj.9.10.53.
  22. Bauer E., Kohavi R. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36, 1999. 105–139. DOI:10.1023/A:1007515423169
Copyright (c) 2022 Viktoriia Kotenko