DEEP Q NETWORK FOR ADAPTIVE PELICAN CROSSING SIGNAL OPTIMIZATION BALANCING PEDESTRIAN SAFETY AND VEHICULAR EFFICIENCY - Chairunnisa, Amalia Yasmin; Kusuma, Andyka; Sumabrata, Jachrizal R (Smart City, 2026)
Static pelican crossing systems often fail to accommodate the stochastic arrival patterns prevalent in high-density transit-oriented development (TOD) zones, creating operational inefficiencies between pedestrian clearance and vehicular flow. At the Cikini Station transit hub in Jakarta, existing fixed signal cycles contribute to pedestrian delays and peak-hour congestion. This study evaluates an adaptive signal control model for mid-block pelican crossings using a Deep Q-Network (DQN) algorithm. To calibrate the simulation, empirical data were extracted from field CCTV footage using the YOLOv8 algorithm, accurately capturing 15-minute peak flow fluctuations and commuter surges. The system is formulated as a Markov Decision Process (MDP) and trained through microscopic simulation in SUMO via TraCI, utilizing real-time queue states to dynamically adjust phase durations. Results indicate that the DQN agent reduced average pedestrian waiting times by 64% (from 28.0 to 10.0 seconds) compared to the static configuration. Furthermore, by enforcing a 3-second all-red clearance and a 6-second evacuation interval, the model eliminated pedestrian risk exposure (0%). Consequently, peak-hour vehicular delay underwent a calculated increase to 33.95 seconds to prioritize safety, yet the intersection maintained a stable Level of Service (LOS). Ultimately, this research provides a data-driven framework for applying reinforcement learning to balance multi-modal mobility demands in high-variance urban traffic environments