<![CDATA[Pedestrian crossing facilities in urban areas are often positioned without adequate consideration of actual movement patterns, leading to low utilization rates, widespread informal crossings, and increased pedestrian-vehicle conflict risks. Traditional manual observation methods for assessing crossing behavior are time-consuming, subjective, and unable to capture continuous spatial-temporal movement dynamics at scale. This study aims to develop and evaluate an automated framework for extracting pedestrian trajectories and assessing the alignment between pedestrian desire lines and existing zebra crossing infrastructure. The methodology integrates YOLO11 fine-tuned object detection with ByteTrack multi-object tracking to process unmanned aerial vehicle (UAV) video data collected at an urban intersection in Surabaya, Indonesia. Pedestrian-vehicle conflict severity was quantified using Time-to-Collision (TTC)-based surrogate safety indicators, including Time Exposed to Time-to-Collision (TET) and Time Integrated Time-to-Collision (TIT). The results reveal substantial heterogeneity in crossing behavior, with distinct spatial clustering of informal crossing hotspots located away from the designated zebra crossing. Asymmetric yet bidirectional pedestrian demand patterns were observed across the study area. Based on trajectory-derived evidence, the study recommends strategic relocation of the zebra crossing approximately 135 meters south to better accommodate natural pedestrian flow and reduce vehicular traffic exposure. These findings demonstrate that deep learning-based trajectory analysis offers a practical, objective, and scalable approach for evidence-based pedestrian infrastructure planning, particularly applicable to rapidly urbanizing contexts in developing countries where conventional assessment resources are limited.]]>
