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All issues Volume 390 (2024) MATEC Web Conf., 390 (2024) 03010 References
Open Access
Issue
MATEC Web Conf.
Volume 390, 2024
3rd International Scientific and Practical Conference “Energy-Optimal Technologies, Logistic and Safety on Transport” (EOT-2023)
Article Number 03010
Number of page(s) 7
Section Modern Technologies of Transportation Organization and Logistics. Interaction of Transport and Manufacturing Enterprises
DOI https://doi.org/10.1051/matecconf/202439003010
Published online 24 January 2024
  1. H.N. Mhaskar, T. Poggio, Deep vs. shallow networks: An approximation theory perspective, Analysis and Applications, 14(6), 829–848 (2016). [CrossRef] [Google Scholar]
  2. K.M. He, X.Y. Zhang, S.Q. Ren, J. Sun, Deep Residual Learning for Image Recognition, in Proc. Conference on Computer Vision and Pattern Recognition, 770–778 (2016). [Google Scholar]
  3. P. Bhavsar, I. Safro, N. Bouaynaya, R. Polikar, D. Dera, Machine Learning in Transportation Data Analytics, Data Analytics for Intelligent Transportation Systems, Elsevier, 283–307 (2017). [Google Scholar]
  4. V. Kotenko, Application of algorithmic models of machine learning to the freight transportation process, Transport Technologies, 3(2), 10–21 (2022). [CrossRef] [Google Scholar]
  5. K. Tsolaki, T. Vafeiadis, A. Nizamis, D. Ioannidis, D. Tzovaras, Utilizing machine learning on freight transportation and logistics applications: A review, ICT Express, 2022 Express (2022). [Google Scholar]
  6. A. Samimi, H. Razi-Ardakani, A. Nohekhan, Comparison between Different Data Mining Algorithms in Freight Mode Choice, American Journal of Applied Sciences, 14(2), 204–216 (2017). [CrossRef] [Google Scholar]
  7. W. Abdelwahab, T. Sayed, Freight mode choice models using artificial neural networks, Civil Engineering and Environmental Systems, 16(4), 267–286 (1999). [CrossRef] [Google Scholar]
  8. A. Tortum, N. Yayla, M. Gökdag, 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 (2009) [CrossRef] [Google Scholar]
  9. S. Van der Spoel, A. Chintan A., J. Van Hillegersberg, Predictive analytics for truck arrival time estimation: a field study at a European distribution center, International Journal of Production Research, 55(17), 5062–5078 (2015). [Google Scholar]
  10. N. Servos, X. Liu, M. Teucke, M. Freitag, Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms, Logistics, 4, 1 (2020). [CrossRef] [Google Scholar]
  11. A. Yakushenko A., D. Shevchuk D., D. Medynskyi, Neural network model for predicting the execution time of a transport task, Science-Based Technologies, 49(1), 33–38 (2021), (in Ukrainian). [Google Scholar]
  12. U. Ahmed, & M.J. Roorda, Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods, Transportation Research Record, 2676(2), 541–552 (2022). [CrossRef] [Google Scholar]
  13. A. Schoen, A. Byerly, B. Hendrix, R. M. Bagwe, E. C. d. Santos, Z. B. Miled, A Machine Learning Model for Average Fuel Consumption in Heavy Vehicles, IEEE Transactions on Vehicular Technology, 68, 7, pp. 6343–6351 (2019). [CrossRef] [Google Scholar]
  14. T. Bousonville, D. C. Kamga, T. Krüger, M. Dirichs, Data driven analysis and forecasting of medium and heavy truck fuel consumption, Enterprise Information Systems (2020). [Google Scholar]
  15. J. Topi´c, B. Škugor, J. Deur, Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data in Sustainability, 14(2), 744 (2022). [CrossRef] [Google Scholar]
  16. A. Singh, A. Das, U. K. Bera, G. M. Lee, Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks, IEEE Access, 9, 103497–103512 (2021). [CrossRef] [Google Scholar]
  17. A. Dobson, An Inroduction to Generalized Linear Models, Boca Raton: Chapman & Hall/CRC, Boca Raton (2002). [Google Scholar]
  18. Documentation H2O, https://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html# [Google Scholar]
  19. E. Bauer, R. Kohavi, An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants, Machine Learning, 36, 105–139 (1999). [CrossRef] [Google Scholar]
  20. V. Kotenko, Machine Learning Algorithmic Models for Forecasting Fuel Consumption by Vehicles of the Grain Crops Delivery, Центральноукраїнський науковий вісник, Технічні науки, 6(37), 1, 173–182 (2022), (in Ukrainian). [Google Scholar]
  21. V Kotenko, Method and Results of the Most Efficient Means of Transport Selection for Executing Orders of the Grain Crops Delivery, in TRANSBALTICA XIII: Transportation Science and Technology, TRANSBALTICA 2022, Lecture Notes in Intelligent Transportation and Infrastructure, 606–617, Springer (2023). [Google Scholar]
  22. Documentation Python 3.9.14, https://www.python.org/downloads/release/python-3914/ last accessed 2023/06/21. [Google Scholar]
  23. Scikit-learn. Machine Learning in Python, https://scikit-learn.org/stable/supervised_learning.html#supervised-learning, last accessed 2023/06/21. [Google Scholar]

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