Variable Duration Traffic Light Scheduling Algorithm based on Real Time Vehicle Density and Waiting Time

Abstract

Rapid population growth and the corresponding rise in vehicle ownership have led to severe traffic congestion, especially at urban junctions where multiple flows intersect. Efficient traffic management at these cross-road junctions is therefore critical for reducing travel delays, fuel consumption, and vehicular emissions. Conventional fixed-time traffic signal systems fail to adapt to dynamic traffic conditions, often resulting in inefficient green-time allocation and prolonged waiting periods. To address these challenges, this study proposes a dynamic traffic light control algorithm that adjusts signal durations in real time based on vehicle density and cumulative waiting time at each approach. The algorithm continuously analyzes incoming traffic data to determine the optimal green phase duration, ensuring balanced queue clearance and improved intersection throughput. Simulation results demonstrate significant performance improvements compared to conventional fixed-time control. The proposed system reduces average trip duration by 4–28%, fuel consumption by 6–25%, and CO₂ emissions by 4–28%, indicating substantial gains in both mobility and environmental sustainability. These findings highlight the potential of adaptive, data-driven signal control as a practical solution for modern urban traffic management systems.