Optimizing Traffic Light Timing Using Graph Theory: A Case Study at Urban Intersections

Abstract

Purpose of the study: This study aims to optimize traffic light timing at the Usman Salengke-Poros Malino-K.H. Wahid Hasyim intersection using a graph theory approach. By modeling compatible traffic flows and calculating optimal signal durations, the study seeks to reduce congestion, minimize delays, and improve traffic efficiency. Methodology: This study utilized manual traffic volume data collection methods with direct field observations at the Usman Salengke-Poros Malino-K.H. Wahid Hasyim intersection. It employed Webster's method for optimal cycle calculation and MATLAB software for simulation. Tools included measuring tapes (Stanley), stopwatches (Casio), and data sheets for recording traffic flow. Surveys captured vehicle types and peak hour volumes. Main Findings: The optimal traffic light cycle duration was calculated as 95 seconds, reducing the original cycle time of 128 seconds. Peak traffic volume was observed at 1,383 pcu/hour (Usman Salengke North). The green light duration increased for Usman Salengke North to 39 seconds and for Poros Malino to 28 seconds. Total average vehicle waiting time decreased by 33.3%, with improved throughput by 20%. Novelty/Originality of this study: This study introduces a practical application of graph theory for optimizing traffic light timing, using compatible flow modeling to simplify intersection analysis. Unlike adaptive systems requiring expensive technology, this approach relies on manual traffic data, offering cost-effective solutions. It advances existing knowledge by providing a simplified, scalable method for reducing congestion and enhancing traffic efficiency in urban settings.