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Received 03.10.2022

Revised 20.02.2023

Accepted 30.03.2023

Retrieved from Vol. 27, No. 1, 2023

Pages 89 -97

  • 141 Views

Suggested citation

Danchuk, V., Svatko, V., Marchenko, V., & Popchenko, Y. (2023). Intelligent Transport Systems as One of the Main Factors in Implementation of the Smart Logistics Concept. The National Transport University Bulletin, 27(1), 89-97. https://doi.org/10.33744/2308-6645-2023-1-55-089-097

Intelligent Transport Systems as One of the Main Factors in Implementation of the Smart Logistics Concept

Viktor Danchuk Vitaly Svatko Vladislav Marchenko Y. Popchenko

Abstract

The paper proposes that currently ITS is the most developed concept of Smart Logistic implementation, and in its essence approaches the essence of the concept of cyber-physical systems (CPS), as intelligent automatic or maximally automated control systems for physical objects and processes of various nature. Through test studies with the help of various methods of artificial intelligence, it was determined that the most effective method of optimizing the route of freight transport is a modified ant algorithm, which allows dynamic routing of logistics flows in real time, taking into account the non-stationary dynamics of transport flows. Its use allows to reduce the time of searching for the optimal solution by an average of 15% and to obtain better results of path optimization in most cases. The authors consider one of the perspective areas of further research to be the creation within the framework of the ITS concept, as CPS for Smart Logistic, of an intelligent support system for transport and logistics management of cargo transportation in real time, taking into account the impact of various external factors on the transportation process.

 

Keywords:

intelligent transport system; smart logistics; cyberphysical system; optimization; artificial intelligence methods of optimization

References

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  2. Zhang, N. (2018). Smart Logistics Path for Cyber-Physical Systems With Internet of Things. IEEE ACCESS, 6, 70808–70819. https://doi.org/10.1109/ACCESS.2018.2879966
  3. Nikitas, A., Michalakopoulou, K., Tchouamou, N. E., Karampatzakis, D. (2020). Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era. Sustainability, 12(2789), 1–19. https://doi.org/10.3390/su12072789
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  5. Sussman, J. M. (2005). Perspectives on Intelligent Transportation Systems (ITS). Springer Science+Business Media, 232 p.
  6. Danchuk, V., Bakulich, O., Svatko, V. (2019). Building Optimal Routes for Cargo Delivery in Megacities. Transport and Telecommunications, 20(2), 142–152. https://doi.org/10.2478/ttj-2019-0013
  7. Dorigo, M., Di Caro, G. (1999). The ant colony optimization meta-heuristic. New Idea in Optimization. McGrow-Hill, 1, 11–32.
  8. Osaba, E., Yang, X.-S., Diaza, F., Lopez-Garcia, P., Carballedo, R. (2016). An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems. Engineering Applications of Artificial Intelligence, 48(C), 59–71. https://doi.org/10.1016/j.engappai.2015.10.006
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https://doi.org/10.33744/2308-6645-2023-1-55-089-097

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