Нейросетевое моделирование пропускной способности улично-дорожной сети
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Neural network modeling of street and road network capacity

Sysoev A.S.,  Pogodaev A.K.,  Klyavin V.E.,  Zhikhoreva S.V.,  Borovkova G.S. 

UDC 04.942:625.7
DOI: 10.26102/2310-6018/2024.47.4.010

  • Abstract
  • List of references
  • About authors

The problems of increasing the number of personal transportation vehicles in urban agglomeration as well as the number of cargos lead to applying Intelligent transportation systems based on Machine Learning techniques and Artificial Intelligence models to create strategies to reduce congestion and to prevent accidents. An important parameter of transportation system showing the effectiveness of using existing urban infrastructure is the capacity of the planned route. The paper is devoted to the creating model of urban route capacity based on the capacities of its elements, they are namely stretches and intersections. The approach to create such model is Mathematical Remodeling, where feed-forward neural network is chosen as a unified class to substitute models of different heterogeneous classes during modeling. It is proposed to use index of route capacity to form data sets for model fitting. The given numerical examples show how the proposed approach can be applied. The capacities of three planned routes are estimated and the best route is chosen, the efficiency criterion is traffic flow volume to capacity ratio. The prospective issue of the presented study is analyzing sensitivity of the created model to identify the parameters of route elements affecting the most to the capacity and to control them increasing the total efficiency of the system.

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Sysoev Anton Sergeevich
Candidate of Technical Sciences, Associate Professor

Lipetsk State Techncal University

Lipetsk, Russia

Pogodaev Anatoly Kiryanovich

Email: pak@stu.lipetsk.ru


Lipetsk, Russia

Klyavin Vladimir Ernstovich


Lipetsk, Russia

Zhikhoreva Svetlana Viktorovna

Email: zhikhoreva_sv@stu.lipetsk.ru


Lipetsk, Russia

Borovkova Galina Sergeevna

Email: haligh@mail.ru


Lipetsk, Russia

Keywords: neural networks, remodeling, capacity estimation, street and road network, mathematical modeling

For citation: Sysoev A.S., Pogodaev A.K., Klyavin V.E., Zhikhoreva S.V., Borovkova G.S. Neural network modeling of street and road network capacity. Modeling, Optimization and Information Technology. 2024;12(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1706 DOI: 10.26102/2310-6018/2024.47.4.010 (In Russ).

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Full text in PDF

Received 06.10.2024

Revised 20.10.2024

Accepted 28.10.2024