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<p>Efficient operation of traffic light control systems plays a critical role in reducing travel time, fuel consumption, and environmental impact, while enhancing overall traffic flow. This study introduces a novel decision-making framework based on a triangular fuzzy neural network (TFNN). First, present the mathematical foundation of triangular fuzzy numbers, the operational laws, and an aggregation operator for triangular fuzzy numbers based on Aczel–Alsina norms. The proposed model is applied to evaluate multiple operational strategies for traffic light control systems using expert judgment data represented as triangular fuzzy numbers. The TFNN processes this data through input, hidden, and output layers to identify the optimal strategy. The proposed model identifies that the vehicle-actuated system is the best traffic light control method. A sensitivity analysis is conducted by varying the Aczel–Alsina parameter to assess the model’s robustness. Finally, the proposed model is compared with other existing decision-making models, and the results show that the proposed model is applicable and reliable for decision support.</p>

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Analyzing the traffic light control systems based on novel fuzzy neural network under triangular fuzzy numbers

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Ihsan Ullah,
  • Saleem Abdullah,
  • Marya Nawaz

摘要

Efficient operation of traffic light control systems plays a critical role in reducing travel time, fuel consumption, and environmental impact, while enhancing overall traffic flow. This study introduces a novel decision-making framework based on a triangular fuzzy neural network (TFNN). First, present the mathematical foundation of triangular fuzzy numbers, the operational laws, and an aggregation operator for triangular fuzzy numbers based on Aczel–Alsina norms. The proposed model is applied to evaluate multiple operational strategies for traffic light control systems using expert judgment data represented as triangular fuzzy numbers. The TFNN processes this data through input, hidden, and output layers to identify the optimal strategy. The proposed model identifies that the vehicle-actuated system is the best traffic light control method. A sensitivity analysis is conducted by varying the Aczel–Alsina parameter to assess the model’s robustness. Finally, the proposed model is compared with other existing decision-making models, and the results show that the proposed model is applicable and reliable for decision support.