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Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models

Author

Listed:
  • Hong Ki An

    (Department of Transportation Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Muhammad Awais Javeed

    (School of Information Engineering, Chang’an University, Xi’an 710064, China)

  • Gimok Bae

    (Division of Smart City Engineering, School of Civil and Environmental Engineering, Daejin University, Pocheon 11159, Korea)

  • Nimra Zubair

    (School of Information Engineering, Chang’an University, Xi’an 710064, China)

  • Ahmed Sayed M. Metwally

    (Department of Mathematics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Patrizia Bocchetta

    (Dipartimento di Ingegneria dell’Innovazione, Università del Salento, Via Monteroni, 73100 Lecce, Italy)

  • Fan Na

    (School of Information Engineering, Chang’an University, Xi’an 710064, China)

  • Muhammad Sufyan Javed

    (School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China)

Abstract

With the development of intelligent transportation systems, research into intelligent traffic signal control has received considerable attention. To date, many traffic signal control models have been studied, where most of the models concentrate on how to minimize travel time, vehicle delay, and the number of stops or how to maximize capacity. This study introduces the Garra Rufa–inspired (GRI) algorithm, which is used to optimize traffic signal control modelling considering the number of vehicles in a queue. GRI has the characteristics of using the decision variables of the code as the operation object, directly using the objective function value for the search information, using multiple search points at the same time, and using probability search technology. Theoretical analysis of intelligent optimization and research into application methods were carried out to resolve the problem of traffic signal optimization control. The output of the GRI algorithm was compared, calibrated, and validated with SIDRA. Furthermore, to obtain more comprehensive results, the genetic algorithm (GA) and particle swarm optimization (PSO) were also compared. The results of the analysis show that the GRI decreases by 10.1% (intersection A) and 16.5% (intersection B) in the number of vehicles in the queue.

Suggested Citation

  • Hong Ki An & Muhammad Awais Javeed & Gimok Bae & Nimra Zubair & Ahmed Sayed M. Metwally & Patrizia Bocchetta & Fan Na & Muhammad Sufyan Javed, 2022. "Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11422-:d:912765
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    References listed on IDEAS

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    Cited by:

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    2. Zahra Zeinaly & Mahdi Sojoodi & Sadegh Bolouki, 2023. "A Resilient Intelligent Traffic Signal Control Scheme for Accident Scenario at Intersections via Deep Reinforcement Learning," Sustainability, MDPI, vol. 15(2), pages 1-26, January.
    3. Suhaib Alshayeb & Aleksandar Stevanovic & Nikola Mitrovic & Elio Espino, 2022. "Traffic Signal Optimization to Improve Sustainability: A Literature Review," Energies, MDPI, vol. 15(22), pages 1-24, November.

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