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Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections

Author

Listed:
  • Sadiqa Jafari

    (Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
    These authors contributed equally to this work.
    Current address: Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea.)

  • Zeinab Shahbazi

    (Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
    These authors contributed equally to this work.)

  • Yung-Cheol Byun

    (Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea)

Abstract

Traffic congestion is a significant issue in many countries today. The suggested method is a novel control method based on multiple intersections considering the kind of traffic light and the duration of the green phase to determine the optimal balance at intersections by using fuzzy logic control, for which the balance should be adaptable to the unchanging behavior of time. It should reduce traffic volume in transport, average waits for each vehicle, and collisions between cars by controlling this balance in response to the typical behavior of time and randomness in traffic conditions. The proposed method is investigated at intersections using a sampling multi-agent system to set traffic light timings appropriately. The program is provided with many intersections, each of which is an independent entity exchanging information with the others. The stability per entity is proven separately. Simulation results show that Takagi–Sugeno (TS) fuzzy modeling performs better than Takagi–Sugeno (TS) fixed-time scheduling in decreasing the length of queueing times for vehicles.

Suggested Citation

  • Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections," Mathematics, MDPI, vol. 10(16), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2832-:d:883952
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    References listed on IDEAS

    as
    1. Zeinab Shahbazi & Yung-Cheol Byun, 2022. "NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security," IJERPH, MDPI, vol. 19(12), pages 1-14, June.
    2. Amer, Hayder M. & Al-Kashoash, Hayder & Hawes, Matthew & Chaqfeh, Moumena & Kemp, Andrew & Mihaylova, Lyudmila, 2019. "Centralized simulated annealing for alleviating vehicular congestion in smart cities," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 235-248.
    3. Muhammad Usman & Wajahat Ullah Khan Tareen & Adil Amin & Haider Ali & Inam Bari & Muhammad Sajid & Mehdi Seyedmahmoudian & Alex Stojcevski & Anzar Mahmood & Saad Mekhilef, 2021. "A Coordinated Charging Scheduling of Electric Vehicles Considering Optimal Charging Time for Network Power Loss Minimization," Energies, MDPI, vol. 14(17), pages 1-16, August.
    4. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
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    Cited by:

    1. Hongyan Dui & Yulu Zhang & Songru Zhang & Yun-An Zhang, 2023. "Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion," Mathematics, MDPI, vol. 11(9), pages 1-17, April.
    2. Krasimira Stoilova & Todor Stoilov, 2023. "Optimizing Traffic Light Green Duration under Stochastic Considerations," Mathematics, MDPI, vol. 11(3), pages 1-25, January.
    3. Anton Agafonov & Alexander Yumaganov & Vladislav Myasnikov, 2023. "Cooperative Control for Signalized Intersections in Intelligent Connected Vehicle Environments," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    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.

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