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<p>In this study we have proposed an algorithmic approach for signal optimization. We used the concept of fuzzy graph theory, fuzzy chromatic number (FCN) and fuzzy inference system (FIS) to assist the traffic light allocations that ensure traffic light assignment based on traffic flow, conflict and the length of queues at the intersection. The algorithmic approach is evaluated by using a case study of Kalma-Chowk Lahore, Pakistan and then the performance of our proposed method is compared with the current signal control system. We obtained that based on our proposed method the total traffic light cycle time can be reduced at identifiable intersections. The most important results of our study are as: A pseudocode of developing a fuzzy graph using intersection traffic data. The algorithm 1 is based on function FCN with partition which is used in the main algorithm. The main algorithm of this study is based on MATLAB and calculates fuzzy graph model data of traffic lights and traffic signal stations within the FCN method. Algorithm 2 is also a MATLAB program, which determines the green light time of every operating phase using the fuzzy inference rules of Mamdani. The third algorithm 3 is based on the set of fuzzy rules which is used in Mamdani FIS.</p>

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An algorithmic approach to fuzzy traffic control by using MATLAB algorithms for signal optimization

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Wakeel Ahmed,
  • Shahid Zaman,
  • Shamsa Mehmood

摘要

In this study we have proposed an algorithmic approach for signal optimization. We used the concept of fuzzy graph theory, fuzzy chromatic number (FCN) and fuzzy inference system (FIS) to assist the traffic light allocations that ensure traffic light assignment based on traffic flow, conflict and the length of queues at the intersection. The algorithmic approach is evaluated by using a case study of Kalma-Chowk Lahore, Pakistan and then the performance of our proposed method is compared with the current signal control system. We obtained that based on our proposed method the total traffic light cycle time can be reduced at identifiable intersections. The most important results of our study are as: A pseudocode of developing a fuzzy graph using intersection traffic data. The algorithm 1 is based on function FCN with partition which is used in the main algorithm. The main algorithm of this study is based on MATLAB and calculates fuzzy graph model data of traffic lights and traffic signal stations within the FCN method. Algorithm 2 is also a MATLAB program, which determines the green light time of every operating phase using the fuzzy inference rules of Mamdani. The third algorithm 3 is based on the set of fuzzy rules which is used in Mamdani FIS.