Analytical Model for Information Flow Management in Intelligent Transport Systems

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.

Authors
Terentev Alexey1 , Marusin Alexey 1, 2 , Evtyukov Sergey1 , Marusin Aleksandr 2 , Shevtsova Anastasia 3 , Zelenov Vladimir4
Journal
Publisher
MDPI AG
Number of issue
15
Language
English
Pages
3371
Status
Published
Volume
11
Year
2023
Organizations
  • 1 St. Petersburg State University of Architecture and Civil Engineering
  • 2 RUDN University (Peoples' Friendship University of Russia Named after Patrice Lumumba)
  • 3 Belgorod State Technological University Named after V.G. Shukhov
  • 4 Plekhanov Russian University of Economics
Keywords
information commutation technologies; intelligent transport systems; Pareto optimal solution; vector optimization techniques; multicriterion problem; mathematical decision-making models
Date of creation
01.07.2024
Date of change
01.07.2024
Short link
https://repository.rudn.ru/en/records/article/record/108039/
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