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.
