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A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies

Author

Listed:
  • Mehran Amini

    (Department of Informatics, Szechenyi Istvan University, 9026 Gyor, Hungary)

  • Miklos F. Hatwagner

    (Department of Informatics, Szechenyi Istvan University, 9026 Gyor, Hungary)

  • Laszlo T. Koczy

    (Department of Informatics, Szechenyi Istvan University, 9026 Gyor, Hungary
    Department of Telecommunication and Media Informatics, Budapest University of Technology and Economics, 1111 Budapest, Hungary)

Abstract

Freeway networks, despite being built to handle the transportation needs of large traffic volumes, have suffered in recent years from an increase in demand that is rarely resolvable through infrastructure improvements. Therefore, the implementation of particular control methods constitutes, in many instances, the only viable solution for enhancing the performance of freeway traffic systems. The topic is fraught with ambiguity, and there is no tool for understanding the entire system mathematically; hence, a fuzzy suggested algorithm seems not just appropriate but essential. In this study, a fuzzy cognitive map-based model and a fuzzy rule-based system are proposed as tools to analyze freeway traffic data with the objective of traffic flow modeling at a macroscopic level in order to address congestion-related issues as the primary goal of the traffic control strategies. In addition to presenting a framework of fuzzy system-based controllers in freeway traffic, the results of this study demonstrated that a fuzzy inference system and fuzzy cognitive maps are capable of congestion level prediction, traffic flow simulation, and scenario analysis, thereby enhancing the performance of the traffic control strategies involving the implementation of ramp management policies, controlling vehicle movement within the freeway by mainstream control, and routing control.

Suggested Citation

  • Mehran Amini & Miklos F. Hatwagner & Laszlo T. Koczy, 2022. "A Combined Approach of Fuzzy Cognitive Maps and Fuzzy Rule-Based Inference Supporting Freeway Traffic Control Strategies," Mathematics, MDPI, vol. 10(21), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4139-:d:964583
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    References listed on IDEAS

    as
    1. Tanzina Afrin & Nita Yodo, 2020. "A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System," Sustainability, MDPI, vol. 12(11), pages 1-23, June.
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    Cited by:

    1. Krasimira Stoilova & Todor Stoilov, 2023. "Optimizing Traffic Light Green Duration under Stochastic Considerations," Mathematics, MDPI, vol. 11(3), pages 1-25, January.

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