This study proposes a software to upgrade the UCF SST\u27s Automated Roadway Conflicts Identification System (ARCIS), a pixel-to-pixel manner automated safety diagnostics and conflict identification system. The system is developed to extract vehicles\u27 trajectories and traffic parameters using unmanned aerial vehicles (UAV) video and utilizing deep learning techniques. A user-friendly tool to improve rapid system development with active-learning, data analysis, and visualization techniques is introduced, which is capable of traffic safety near-miss diagnostics based on the ARCIS output. Multiple approaches are used to enhance the system performance, including video stabilization, object filtering, stitching multiple videos, vehicle detection and tracing. In addition, the active learning technique based on Stream-Based Selective Sampling strategy is adopted for a human-in-the loop label correction that is developed in order to reduce the labeling time and cost. The system outputs 3D maps of vehicle speed, count and surrogate safety measures, which provide insights for traffic safety diagnosis. Ultimately, these functionalities were integrated into a comprehensive system for traffic safety applications. Previous studies only investigated methods for enhancing road traffic safety and traffic network data analysis; this study builds upon the literature but improves upon it with an efficient video processing methodology, a higher quality and accuracy result on traffic trajectory data, and the ability to visualize the data in various formats for traffic analysis
