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Heuristic Fuzzy Approach to Traffic Flow Modelling and Control on Urban Networks

Author

Listed:
  • Alexander Gegov

    (School of Computing, University of Portsmouth, Portsmouth P01 3HE, UK
    English Faculty of Engineering, Technical University of Sofia, 1000 Sofia, Bulgaria)

  • Boriana Vatchova

    (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria)

  • Yordanka Boneva

    (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria)

  • Alexandar Ichtev

    (Department of Systems and Control, Technical University of Sofia, 1000 Sofia, Bulgaria)

Abstract

Computer-aided transport modelling is essential for testing different control strategies for traffic lights. One approach to modelling traffic control is by heuristically defining fuzzy rules for the control of traffic light systems and applying them to a network of hierarchically dependent crossroads. In this paper, such a network is investigated through modelling the geometry of the network in the simulation environment Aimsun. This environment is based on real-world traffic data and is used in this paper with the MATLAB R2019a-Fuzzy toolbox. It focuses on the development of a network of intersections, as well as four fuzzy models and the behaviour of these models on the investigated intersections. The transport network consists of four intersections. The novelty of the proposed approach is in the application of heuristic fuzzy rules to the modelling and control of traffic flow through these intersections. The motivation behind the use of this approach is to address inherent uncertainties using a fuzzy method and analyse its main findings in relation to a classical deterministic approach.

Suggested Citation

  • Alexander Gegov & Boriana Vatchova & Yordanka Boneva & Alexandar Ichtev, 2025. "Heuristic Fuzzy Approach to Traffic Flow Modelling and Control on Urban Networks," Future Internet, MDPI, vol. 17(5), pages 1-19, May.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:5:p:227-:d:1660049
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    References listed on IDEAS

    as
    1. Yaming Guo & Ke Zhang & Xiqun Chen & Meng Li, 2023. "Proactive Coordination of Traffic Guidance and Signal Control for a Divergent Network," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
    2. Yona Elbaum & Alexander Novoselsky & Evgeny Kagan, 2022. "A Queueing Model for Traffic Flow Control in the Road Intersection," Mathematics, MDPI, vol. 10(21), pages 1-15, October.
    3. Krasimira Stoilova & Todor Stoilov, 2023. "Optimizing Traffic Light Green Duration under Stochastic Considerations," Mathematics, MDPI, vol. 11(3), pages 1-25, January.
    4. Boriana Vatchova & Yordanka Boneva, 2023. "Design of Fuzzy and Conventional Controllers for Modeling and Simulation of Urban Traffic Light System with Feedback Control," Mathematics, MDPI, vol. 11(2), pages 1-11, January.
    5. Huang, Ruqing & Han, Lee D. & Huang, Zhongxiang, 2022. "A new network equilibrium flow model: User-equilibrium with quantity adjustment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    Full references (including those not matched with items on IDEAS)

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