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Analytical Model for Information Flow Management in Intelligent Transport Systems

Author

Listed:
  • Alexey Terentyev

    (Department of Vehicles, St. Petersburg State University of Architecture and Civil Engineering, 190005 St. Petersburg, Russia)

  • Alexey Marusin

    (Department of Technical Operation of Vehicles, St. Petersburg State University of Architecture and Civil Engineering, 190005 St. Petersburg, Russia
    Department of Transportation of the Academy of Engineering, RUDN University (Peoples’ Friendship University of Russia Named after Patrice Lumumba), 117198 Moscow, Russia)

  • Sergey Evtyukov

    (Department of Ground Transport and Technological Machines, St. Petersburg State University of Architecture and Civil Engineering, 190005 St. Petersburg, Russia)

  • Aleksandr Marusin

    (Department of Transportation of the Academy of Engineering, RUDN University (Peoples’ Friendship University of Russia Named after Patrice Lumumba), 117198 Moscow, Russia)

  • Anastasia Shevtsova

    (Department of Operation and Organization of Vehicle Traffic, Belgorod State Technological University Named after V.G. Shukhov, 308012 Belgorod, Russia)

  • Vladimir Zelenov

    (Engineering Center, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

Abstract

The performance of this study involves the use of the zoning method based on the principle of the hierarchical relationship between probabilities. This paper proposes an analytical model allowing for the design of information and analysis platforms in intelligent transport systems. The proposed model uses a synthesis of methods for managing complex systems’ structural dynamics and solves the problem of achieving the optimal balance between the information situations existing for the object and the subject under analysis. A series of principles are formulated that govern the mathematical modeling of information and analysis platforms. Specifically, these include the use of an object-oriented approach to forming the information space of possible decisions and the division into levels and subsystems based on the principles of technology homogeneity and information state heterogeneity. Using the proposed approach, an information and analysis platform is developed for sustainable transportation system management, that allows for the objective, multivariate forecasting-based record of changes in the system’s variables over time for a particular process, and where decision-making simulation models can be adjusted in relation to a particular process based on an information situation existing for a particular process within a complex transport system. The study demonstrates a mathematical model that solves the optimal balance problem in organizationally and technically complex management systems and is based on vector optimization techniques for the most optimal decision-making management. The analysis involves classical mathematical functions with an unlimited number of variables including traffic volume, cargo turnover, safety status, environmental performance, and related variables associated with the movement of objects within a transport network. The study has produced a routing protocol prescribing the optimal vehicle trajectories within an organizationally and technically complex system exposed to a substantial number of external factors of uncertain nature.

Suggested Citation

  • Alexey Terentyev & Alexey Marusin & Sergey Evtyukov & Aleksandr Marusin & Anastasia Shevtsova & Vladimir Zelenov, 2023. "Analytical Model for Information Flow Management in Intelligent Transport Systems," Mathematics, MDPI, vol. 11(15), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3371-:d:1208524
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    References listed on IDEAS

    as
    1. Vladimir Shepelev & Alexandr Glushkov & Tatyana Bedych & Tatyana Gluchshenko & Zlata Almetova, 2021. "Predicting the Traffic Capacity of an Intersection Using Fuzzy Logic and Computer Vision," Mathematics, MDPI, vol. 9(20), pages 1-19, October.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Vladimir Shepelev & Sergei Aliukov & Kseniya Nikolskaya & Salavat Shabiev, 2020. "The Capacity of the Road Network: Data Collection and Statistical Analysis of Traffic Characteristics," Energies, MDPI, vol. 13(7), pages 1-18, April.
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