Quantum-Inspired Firefly Algorithm: An Enhanced Approach To Traffic Signal Optimization

In the contemporary era of urbanization, efficient traffic management is paramount to mitigate congestion and enhance mobility. Traditional Traffic Light Controllers (TLCs) are limited by their pre-defined hardware configurations, restricting adaptability to real-time traffic conditions. To overcome these limitations, dynamic traffic signal controllers have been developed for more adaptive and efficient management. This research introduces a Quantum-Inspired Firefly Algorithm (QIFA) for traffic signal optimization, combining quantum computing principles with the Firefly Algorithm (FA) to improve search capabilities. Meta-heuristic algorithms are optimization techniques designed to explore large search spaces and locate near-optimal solutions efficiently, making them suitable for complex problems. Quantum computing leverages quantum mechanics principles to process information in ways classical computing cannot, enhancing speed and problem-solving efficiency. QIFA integrates quantum computing strengths with FA’s nature-inspired behavior to effectively optimize traffic light timings. This hybrid approach aims to reduce congestion and improve traffic flow. The study discusses QIFA’s benefits, drawbacks, and potential performance in various traffic scenarios, emphasizing the importance of parameter tuning for optimal results. Initial simulations with real traffic data indicate that QIFA significantly enhances traffic signal optimization compared to traditional methods. The algorithm exhibits rapid convergence, achieving optimal solutions swiftly, making it viable for real-time applications. This research compares QIFA with conventional meta-heuristic and classical optimization methods, highlighting its superior efficiency and potential for urban traffic management. Although initial findings are encouraging, more investigation is required to confirm its efficacy in a wider range of real-world contexts and at greater dimensions.