Design and Evaluation of Optimization Algorithms for Adaptive Traffic Signal Control and Scalable Simulation of Large-Scale Traffic Networks

Abstract

This study presents the design, implementation, and performance evaluation of optimization algorithms for adaptive traffic signal control within the context of large-scale traffic network simulation. The rapid urbanization and increasing vehicle density demand intelligent traffic management systems that can adapt in real-time to fluctuating traffic conditions. To address this challenge, we propose a set of model-driven optimization techniques aimed at minimizing delays, reducing congestion, and improving traffic throughput by dynamically adjusting signal timings based on prevailing traffic states. The core framework integrates adaptive signal control logic with scalable simulation methodologies to accurately represent traffic behavior across extensive urban networks. Simulation experiments are conducted using representative network topologies under varying traffic demand scenarios to assess the robustness and flexibility of the algorithms. Key performance metrics-including average delay, throughput, queue lengths, and computational efficiency-are used to evaluate the system's accuracy, scalability, and real-time feasibility. The results demonstrate that the proposed optimization algorithms significantly outperform fixed-time and traditional signal control methods, particularly under non-uniform and peak traffic conditions. Moreover, the scalable simulation framework ensures reliable performance analysis even in high-density, multi-intersection environments. This research provides a foundation for future development of intelligent transportation systems and smart city traffic infrastructure based on adaptive, data-driven control strategies.