<![CDATA[Traffic management at at-grade pedestrian crossing facilities (pelican crossings) in highly populated areas, such as the Universitas Indonesia Station, faces significant inefficiency challenges. During peak hours, the fixed-time system is frequently disabled and replaced with subjective manual control by security personnel, thereby triggering irregular stop-and-go cycles and a high accumulation of vehicle delays. This study aims to develop a hybrid adaptive control model integrating Computer Vision, Genetic Algorithm (GA), and Fuzzy Logic to optimize intersection performance under mixed traffic conditions. The research methodology begins with the extraction of traffic and pedestrian characteristic data, calculated manually through recorded field observations. This manual extraction approach is conducted to ensure the accuracy of the baseline data (ground truth) and to avoid machine detection errors during the initial modeling phase. The precise empirical data is then used to calibrate the mathematical analytical model. Subsequently, a Genetic Algorithm is employed offline to determine the optimal delay equilibrium weights between vehicles and pedestrians. This equilibrium foundation is then integrated with Fuzzy Logic, which acts as a dynamic green time extension mechanism for the Computer Vision-based smart system architecture. Macroscopic evaluation results demonstrate that this hybrid adaptive model successfully performs cycle consolidation, effectively reducing the total number of signal cycles from 19 to 10 compared to the existing manual control. During the morning peak session, the system reduced cumulative vehicular delay by 13.03%, successfully saving a total of 737 seconds. This increase in vehicular efficiency necessitated a minor operational trade-off, resulting in a 5.17% increase in average pedestrian waiting time (from 15.29 to 16.06 seconds per person). Crucially, this adjustment remains strictly within the 18-second ideal safety tolerance threshold, proving that the model successfully achieves an ideal operational equilibrium between competing traffic demands.]]>
