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Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision

Author

Listed:
  • Roman Ekhlakov

    (Data Analysis and Machine Learning Department, Financial University under the Government of the Russian Federation, Leningradsky pr-t 49, Moscow 125167, Russia)

  • Nikita Andriyanov

    (Data Analysis and Machine Learning Department, Financial University under the Government of the Russian Federation, Leningradsky pr-t 49, Moscow 125167, Russia)

Abstract

Overloading of network structures is a problem that we encounter every day in many areas of life. The most associative structure is the transport graph. In many megacities around the world, the so-called intelligent transport system (ITS) is successfully operating, allowing real-time monitoring and making changes to traffic management while choosing the most effective solutions. Thanks to the emergence of more powerful computing resources, it has become possible to build more complex and realistic mathematical models of traffic flows, which take into account the interactions of drivers with road signs, markings, and traffic lights, as well as with each other. Simulations using high-performance systems can cover road networks at the scale of an entire city or even a country. It is important to note that the tool being developed is applicable to most network structures described by such mathematical apparatuses as graph theory and the applied theory of network planning and management that are widely used for representing the processes of organizing production and enterprise management. The result of this work is a developed model that implements methods for modeling the behavior of traffic flows based on physical modeling and machine learning algorithms. Moreover, a computer vision system is proposed for analyzing traffic on the roads, which, based on vision transformer technologies, provides high accuracy in detecting cars, and using optical flow, allows for significantly faster processing. The accuracy is above 90% with a processing speed of more than ten frames per second on a single video card.

Suggested Citation

  • Roman Ekhlakov & Nikita Andriyanov, 2024. "Multicriteria Assessment Method for Network Structure Congestion Based on Traffic Data Using Advanced Computer Vision," Mathematics, MDPI, vol. 12(4), pages 1-27, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:4:p:555-:d:1337753
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    References listed on IDEAS

    as
    1. Vladimir Shepelev & Aleksandr Glushkov & Ivan Slobodin & Mohammed Balfaqih, 2023. "Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions," Mathematics, MDPI, vol. 11(16), pages 1-30, August.
    2. Dudu Guo & Yang Wang & Shunying Zhu & Xin Li, 2023. "A Vehicle Detection Method Based on an Improved U-YOLO Network for High-Resolution Remote-Sensing Images," Sustainability, MDPI, vol. 15(13), pages 1-15, June.
    3. Naif Al Mudawi & Asifa Mehmood Qureshi & Maha Abdelhaq & Abdullah Alshahrani & Abdulwahab Alazeb & Mohammed Alonazi & Asaad Algarni, 2023. "Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences," Sustainability, MDPI, vol. 15(19), pages 1-19, October.
    4. Kai Zhang & Zixuan Chu & Jiping Xing & Honggang Zhang & Qixiu Cheng, 2023. "Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model," Mathematics, MDPI, vol. 11(19), pages 1-20, September.
    5. Kontorinaki, Maria & Spiliopoulou, Anastasia & Roncoli, Claudio & Papageorgiou, Markos, 2017. "First-order traffic flow models incorporating capacity drop: Overview and real-data validation," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 52-75.
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