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Predicting the Traffic Capacity of an Intersection Using Fuzzy Logic and Computer Vision

Author

Listed:
  • Vladimir Shepelev

    (Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia)

  • Alexandr Glushkov

    (Department of Mathematical and Computer Modelling, South Ural State University (National Research University), 454080 Chelyabinsk, Russia)

  • Tatyana Bedych

    (Department of Energy and Mechanical Engineering, M. Dulatov Kostanay Engineering and Economic University, Kostanay 110000, Kazakhstan)

  • Tatyana Gluchshenko

    (Department of Electric Power, Kostanay Regional University named after A. Baitursynov, Kostanay 110000, Kazakhstan)

  • Zlata Almetova

    (Department of Automobile Transportation, South Ural State University (National Research University), 454080 Chelyabinsk, Russia)

Abstract

This paper presents the application of simulation to assess and predict the influence of random factors of pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection during a right turn. The data were collected from the surveillance cameras of 25 signal-controlled intersections in the city of Chelyabinsk, Russia, and interpreted by a neural network. We considered the influence of both the intensity of the pedestrian flow and its continuity on the traffic capacity of a signal-controlled intersection in the stochastic approach, provided that the flow of vehicles is redundant. We used a reasonably minimized regression model as the basis for our intersection models. At the first stage, we obtained and tested a simulated continuous-stochastic intersection model that accounts for the dynamics of traffic flow. The second approach, due to the unpredictability of pedestrian flow, used a relevant method for analysing traffic flows based on the fuzzy logic theory. The second was also used as the foundation to build and graphically demonstrate a computer model in the fuzzy TECH suite for predictive visualization of the values of a traffic flow crossing a signal-controlled intersection. The results of this study can contribute to understanding the real conditions at a signal-controlled intersection and making grounded decisions on its focused control.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2631-:d:659148
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    References listed on IDEAS

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    1. H. Echab & H. Ez-Zahraouy, 2017. "Dynamic characteristics of traffic flow with consideration of crossing pedestrians’ behavior at a nonsignalized T-shaped intersection," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(11), pages 1-18, November.
    2. Jiri Horak & Jan Tesla & David Fojtik & Vit Vozenilek, 2019. "Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava," Sustainability, MDPI, vol. 11(24), pages 1-25, December.
    3. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
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    Cited by:

    1. Vladimir Shepelev & Aleksandr Glushkov & Ivan Slobodin & Yuri Cherkassov, 2023. "Measuring and Modelling the Concentration of Vehicle-Related PM2.5 and PM10 Emissions Based on Neural Networks," Mathematics, MDPI, vol. 11(5), pages 1-23, February.
    2. 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.
    3. Vladimir Shepelev & Alexandr Glushkov & Olga Fadina & Aleksandr Gritsenko, 2022. "Comparative Evaluation of Road Vehicle Emissions at Urban Intersections with Detailed Traffic Dynamics," Mathematics, MDPI, vol. 10(11), pages 1-19, May.
    4. Daniel Doz & Darjo Felda & Mara Cotič, 2023. "Demographic Factors Affecting Fuzzy Grading: A Hierarchical Linear Regression Analysis," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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