LOGISTICS MANAGEMENT IN ACCORDANCE WITH THE DEMAND FOR PASSENGER AND FREIGHT TRANSPORTATION WITH THE ADVENT OF ARTIFICIAL INTELLIGENCE (EXPERIENCE FOR UKRAINE)
Keywords:
artificial intelligence, transportation and logistics industry, demand forecasting, optimization of technological and economic processes, deep machine learning.
Abstract
Introduction. The research aims to determine the potential for optimizing the transportation and logistics industry in Ukraine using artificial intelligence. The implementation of systematic organizational solutions based on artificial intelligence, particularly for forecasting and meeting the demand for passenger and freight transportation, can contribute to the technological and economic optimization of the industry. Objective (Aim). The objective of this article is to conduct a forecaststatistical assessment of the impact of integrating artificial intelligence into the transportation and logistics sector in Ukraine. The use of systemic models and artificial intelligence tools based on deep machine learning enables the prediction and optimization of industry development. Results. The results of the study indicate the potential for implementing systemic solutions using artificial intelligence in Ukraine’s transportation and logistics industry. These solutions can lead to positive developments in the transportation sector, including the possibility of overcoming an extended period of recession. The research findings have practical implications for the development of mechanisms and organizationaltechnological solutions for the implementation of advanced technologies in the fourth wave of industrial development and global digitalization. Conclusions. In conclusion, the introduction of artificial intelligence in Ukraine’s transportation and logistics sector has the potential to enhance efficiency and optimize passenger and freight transportation processes. The proposed model and tools for implementing artificial intelligence, particularly based on deep machine learning, can serve as a foundation for further development in the transportation industry. Future research should focus on improving forecast-optimization models, including the development of more accurate models for predicting demand for passenger and freight transportation. Advancements in artificial intelligence technology and the use of new machine learning algorithms can help refine forecast models and their application in the transportation and logistics industry.
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References
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10. Babayigit B., Gürbüz F., Denizhan B. Logistics performance index estimating with artificial intelligence. International journal of shipping and transport logistics. 2023. Vol. 16, no. 3/4. P. 360. URL: https://doi. org/10.1504/ijstl.2023.129876 (date of access: 08.07.2023).
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13. Mulongo N. Y., Mnkandla E., Kanakana-Katumba G. Artificial intelligence as key driver for competitiveness in the railway industry: review. 2021 62nd international scientific conference on information technology and management science of riga technical university (ITMS), Riga, Latvia, 14–15 October 2021. 2021. URL: https://doi.org/10.1109/ itms52826.2021.9615314 (date of access: 08.07.2023).
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15. Ulnicane I. Emerging technology for economic competitiveness or societal challenges? Framing purpose in Artificial Intelligence policy. Global public policy and governance. 2022. URL: https://doi.org/10.1007/s43508- 022-000498 (date of access: 08.07.2023).
16. Kolodiichuk V., Cherevko H., Popivniak R. Quality assessment of transit potential of the transport–logistics system of ukraine. Global business review. 2020. P. 097215092090700. URL: https://doi.org/10.1177/ 0972150920907008 (date of access: 08.07.2023).
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18. Liashenko V., Ivanov S., Trushkina N. A conceptual approach to forming a transport and logistics cluster as a component of the region’s innovative infrastructure (on the example of prydniprovsky economic region of ukraine). Virtual economics. 2021. Vol. 4, no. 1. P. 19–53. URL: https://doi.org/10.34021/ve.2021.04.01(2) (date of access: 08.07.2023).
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21. A Physics-informed road user safety field theory for traffic safety assessments applying artificial intelligence-based video analytics / A. Arun et al. Analytic Methods in Accident Research. 2023. P. 100252. URL: https://doi.org/10.1016/j.amar.2022.100252 (date of access: 08.07.2023).
22. Zachariah R. A., Sharma S., Kumar V. Systematic review of passenger demand forecasting in aviation industry. Multimedia tools and applications. 2023. URL: https://doi.org/10.1007/s11042-023-15552-1 (date of access: 08.07.2023).
23. A data analytics framework for reliable bus arrival time prediction using artificial neural networks / E. Hassannayebi et al. International journal of data science and analytics. 2023. URL: https://doi.org/10.1007/ s41060-023-00391y (date of access: 08.07.2023).
24. Railway freight demand forecasting based on multiple factors: grey relational analysis and deep autoencoder neural networks / C. Liu et al. Sustainability. 2023. Vol. 15, no. 12. P. 9652. URL: https://doi.org/10.3390/ su15129652 (date of access: 08.07.2023).
25. Machine learning applied to public transportation by bus: a systematic literature review / T. Alexandre et al. Transportation research record: journal of the transportation research board. 2023. P. 036119812311551. URL: https://doi.org/10.1177/03611981231155189 (date of access: 08.07.2023).
26. Machine learning for air transport planning and management / G. Wild et al. AIAA AVIATION 2022 forum, Chicago, IL & Virtual. Reston, Virginia, 2022. URL: https://doi.org/10.2514/6.2022-3706 (date of access: 08.07.2023).
27. Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model / J. Chen et al. International journal of shipping and transport logistics. 2021. Vol. 13, no. 5. P. 538. URL: https://doi.org/10.1504/ijstl.2021.117278 (date of access: 08.07.2023).
28. Forecasting the international air passengers of Iran using an artificial neural network / F. Nourzadeh et al. International journal of industrial and systems engineering. 2020. Vol. 34, no. 4. P. 562. URL: https://doi. org/10.1504/ijise.2020.106089 (date of access: 08.07.2023).
29. Traffic flow modelling of long and short trucks using a hybrid artificial neural network optimized by particle swarm optimization / I. Oyeyemi Olayode et al. International journal of transportation science and technology. 2023. URL: https://doi.org/10.1016/j.ijtst.2023.04.004 (date of access: 08.07.2023).
30. Hu Y.-C. Air passenger flow forecasting using nonadditive forecast combination with grey prediction. Journal of air transport management. 2023. Vol. 112. P. 102439. URL: https://doi.org/10.1016/j.jairtraman.2023. 102439 (date of access: 08.07.2023).
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33. Nigam R., Govinda K. Cloud based flight delay prediction using logistic regression. 2017 international conference on intelligent sustainable systems (ICISS), Palladam, 7–8 December 2017. 2017. URL: https://doi. org/10.1109/iss1.2017.8389254 (date of access: 08.07.2023).
Published
2023-10-12
How to Cite
Harkava, V. (2023). LOGISTICS MANAGEMENT IN ACCORDANCE WITH THE DEMAND FOR PASSENGER AND FREIGHT TRANSPORTATION WITH THE ADVENT OF ARTIFICIAL INTELLIGENCE (EXPERIENCE FOR UKRAINE). Transport Development, (3(18), 9-23. https://doi.org/10.33082/td.2023.3-18.01
Section
ECONOMY
