Abstract
The article presents the results of the analysis of existing national intelligent transport systems in the member states of the Eurasian Economic Union (EAEU). Currently, the development of intelligent transport systems (ITSs) is significantly limited by the difficulty of creating the control part of the system, which, except for the simplest cases of linear second-order objects, requires the use of functional converters of many variables or complex computing devices that solve the boundary problem. The authors have developed a scheme illustrating the realization of ITS optimal control based on a number of principles. This scheme shows the principal possibility of constructing ITSs of optimal control of n-order objects in which a set of predictive devices is used as the optimal regulator. World experience shows that one of the most important elements of the economy of states is the transport infrastructure. It largely determines the scale of production and trade. Due to the increasing requirements for the quality of automatic control processes in the transport infrastructure, ITSs are increasingly being used. ITS is the transport management using the information infrastructure. In other words, it is the use of a control system and an extensive class of speed-optimal systems. The purpose of the study is the development of ITSs in the EAEU countries by using the method of optimal management and forecasting. The paper is structured as follows. In Sect. 1, we describe the state of ITSs in the EAEU countries. In Sect. 2, we present a block diagram of the optimal ITS control system with single-coordinate prediction. Section 3 provides a description of various studies. And in Sect. 4, we conclude on the application of the optimal control and forecasting method.
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References
Abbasi, M., Najafi, A., Rafiee, M., Khosravi, M. R., Menon, V. G., & Muhammad, G. (2021). Efficient flow processing in 5g-envisioned SDN-based internet of vehicles using GPUs. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5283–5292.
Agarwal, P., Chopra, K., Kashif, M., & Kumari, V. (2018). Implementing ALPR for detection of traffic violations: A step towards sustainability. Procedia Computer Science, 132, 738–743. https://doi.org/10.1016/j.procs.2018.05.085
Chupin, A. L., Chupina, Z. S., Morozova N. N., Vorotyntseva T. M., & Levinskay, E. V. (2019). Prediction model of the efficacy and the implementation time of transportation intelligent systems. In IOP Conference Series: Materials Science and Engineering. II International Scientific Practical Conference Breakthrough Technologies and Communications in Industry and City, BTCI 2019 (p. 012006). https://doi.org/10.1088/1757-899X/828/1/012006
Evtiukov, S. A., Evtiukov, S. S., & Kurakina, E. V. (2020). Smart transport in road transport infrastructure. In IOP Conference Series: Materials Science and Engineering (Vol. 832, p. 012094). https://doi.org/10.1088/1757-899X/832/1/012094
Giuffrida, M., Perotti, S., Tumino, A., & Villois, V. (2021). Developing a prototype platform to manage intelligent communication systems in intermodal transport. Transportation Research Procedia, 55, 1320–1327. https://doi.org/10.1016/j.trpro.2021.07.116
Khamehchi, E., Mahdiani, M. R., Amooie, M. A., & Hemmati-Sarapardeh, A. (2020). Modeling viscosity of light and intermediate dead oil systems using advanced computational frameworks and artificial neural networks. Journal of Petroleum Science and Engineering, 193https://doi.org/10.1016/j.petrol.2020.107388
Mammadov, I. B. ogly (2021). The Caspian vector of transport and logistics policy of the EAEU. Contours of Global Transformations: Politics, Economy, Law, 14(5), 177–192. https://doi.org/10.23932/2542-0240-2021-14-5-9
Mavrin, V., Magdin, K., Shepelev, V., & Danilov, I. (2020). Reduction of environmental impact from road transport using analysis and simulation methods. Transportation Research Procedia, 50, 451–457. https://doi.org/10.1016/j.trpro.2020.10.053
Temirbekova, A., & Dulambayeva, R. (2022). Development of the transport industry of the EAEU countries in the conditions of the pandemic. Transportation Research Procedia, 63, 1389–1395. https://doi.org/10.1016/j.trpro.2022.06.149
Ministry of Investment and Development of the Republic of Kazakhstan. http://mid.gov.kz/
Ministry of Transport and Roads of the Kyrgyz Republic (MTR). http://www.mtd.gov.kg/
Munusamy, M., Adhikari, M. A., Khan, V. G., Menon, S. N., Srirama, L. T., & Alex, M. R. (2021). Edge-centric secure service provisioning in IoT-enabled maritime transportation systems. IEEE Trans. Intell. Transp. Syst., 1–10.https://doi.org/10.1109/TITS.2021.3102957
Muratova, E., Kravchenko, E., Sukhoveeva, A., & Andreeva, O. (2021). Information space of the economic management system in the business management system. In E3S Web Conferences (Vol. 273, p. 08088). https://doi.org/10.1051/e3sconf/202127308088
Pak, A. Y., Chupina, Z. S., Chupin, A. L., Fattyakhetdinova, V. R., & Shobekova, Sh. U. (2020, June 20). Methodological approach to the choice of a set of means for the automation of transport control operations at road checkpoints across the customs border of the EAEU (pp. 151–163). In: Economic strategies of EEU: problems and innovations. Proceedings of the III All-Russian Scientific-Practical Conference.
Resolution of the Council of Ministers of the Republic of Belarus of 14.10.2022 No. 692 “On amendments to the resolutions of the Council of Ministers of the Republic of Belarus of March 17, 2016. No. 206 and June 15, 2019 No. 395”. https://www.alta.ru/tamdoc/22bl0692/
Safiullin, R., Fedotov, V., & Marusin, A. (2020). Method to evaluate performance of measurement equipment in automated vehicle traffic control systems. Transportation Research Procedia, 50, 20–27. https://doi.org/10.1016/j.trpro.2020.10.003
Seliverstov, S. A, Seliverstov, Y. A., Titov, V. O., Vydrina, E. O, Gulyaevsky, S. E., & Vashchuk, A. E. (2020). Development of the structural scheme of the marine intelligent transport system of the Arctic region. Marine Intellectual Technologies, 1–1(47), 84–98.
Sysoev, A., Khabibullina, E., Kadasev, D., & Voronin, N. (2020). Heterogeneous data aggregation schemes to determine traffic flow parameters in regional intelligent transportation systems. Transportation Research Procedia, 45, 507–513. https://doi.org/10.1016/j.trpro.2020.03.063
The concept of forming the Common Transport Space of the Eurasian Economic Community: approved by the decision of the Interstate Council of the Eurasian Economic Community of January 25, 2008 No. 374. http://www.evrazes.com/docs/view/68
Trofimenko, Y., Komkov, V., & Trofimenko, K. (2020). Forecast of energy consumption and greenhouse gas emissions by road transport in Russia up to 2050. Transportation Research Procedia, 50, 698–707. https://doi.org/10.1016/j.trpro.2020.10.082
Zhou, S., Sui, S., & Tong, S. (2022). Adaptive neural networks optimal control of permanent magnet synchronous motor system with state constraints. Neurocomputing, 504, 132–140. https://doi.org/10.1016/j.neucom.2022.06.114
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Chupin, A., Afonin, P., Morkovkin, D. (2023). Building Intelligent Transport Systems of the Eurasian Economic Union Based on Optimal Management and Forecasting. In: Kumar, V., Kyriakopoulos, G.L., Akberdina, V., Kuzmin, E. (eds) Digital Transformation in Industry . DTI 2022. Lecture Notes in Information Systems and Organisation, vol 61. Springer, Cham. https://doi.org/10.1007/978-3-031-30351-7_20
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