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Studying the Relationship between the Traffic Flow Structure, the Traffic Capacity of Intersections, and Vehicle-Related Emissions

Author

Listed:
  • Vladimir Shepelev

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

  • Aleksandr Glushkov

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

  • Ivan Slobodin

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

  • Mohammed Balfaqih

    (Department of Computer and Network Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia)

Abstract

This paper proposes a new approach to assessing the impact of changes in the traffic flow on pollutant emissions and the traffic capacity of signal-controlled intersections. We present an intelligent vision system tailored to monitor the traffic behavior at signal-controlled intersections in urban areas. Traffic cameras are used to collect real-time vehicle traffic data. Our system provides valuable insight into the relationship between traffic flows, emissions, and intersection capacity. This study shows how changes in the traffic composition reduce the traffic capacity of intersections and increase emissions, especially those involving fine dust particles. Using the combination of fuzzy logic methods and Gaussian spline distribution functions, we demonstrate the variability of these relationships and highlight the need to further study compromises between mobility and air quality. Ultimately, our results offer promising opportunities for the development of intelligent traffic management systems aimed at balancing the demands of urban mobility while minimizing environmental impact. This study demonstrates the importance of taking into account the correlation between the change in the composition of traffic queues due to a random change in the traffic flow and its impact on emissions and the traffic capacity of intersections. This study found that the presence of various groups of vehicles and their position in the queue can reduce the traffic capacity by up to 70% and increase the growth of harmful emissions by 14 fold.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3591-:d:1220517
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    References listed on IDEAS

    as
    1. Li Song & Wei (David) Fan, 2023. "Intersection capacity adjustments considering different market penetration rates of connected and automated vehicles," Transportation Planning and Technology, Taylor & Francis Journals, vol. 46(3), pages 286-303, April.
    2. 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.
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    Citations

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    Cited by:

    1. 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.
    2. Gongquan Zhang & Fangrong Chang & Helai Huang & Zilong Zhou, 2024. "Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization," Mathematics, MDPI, vol. 12(13), pages 1-24, June.

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