DRIFT open dataset: A drone-derived intelligence for traffic analysis in urban environment
This dataset addresses the need for reliable traffic data for urban mobility research and management, offering a new resource for academic and practical applications, though it is incremental as it builds on existing data collection methods.
The study introduces the DRIFT dataset, a large-scale urban traffic dataset collected from synchronized drone videos, providing 81,699 high-resolution vehicle trajectories to analyze traffic at multiple scales from individual maneuvers to network flow dynamics.
Reliable traffic data are essential for understanding urban mobility and developing effective traffic management strategies. This study introduces the DRone-derived Intelligence For Traffic analysis (DRIFT) dataset, a large-scale urban traffic dataset collected systematically from synchronized drone videos at approximately 250 meters altitude, covering nine interconnected intersections in Daejeon, South Korea. DRIFT provides high-resolution vehicle trajectories that include directional information, processed through video synchronization and orthomap alignment, resulting in a comprehensive dataset of 81,699 vehicle trajectories. Through our DRIFT dataset, researchers can simultaneously analyze traffic at multiple scales - from individual vehicle maneuvers like lane-changes and safety metrics such as time-to-collision to aggregate network flow dynamics across interconnected urban intersections. The DRIFT dataset is structured to enable immediate use without additional preprocessing, complemented by open-source models for object detection and trajectory extraction, as well as associated analytical tools. DRIFT is expected to significantly contribute to academic research and practical applications, such as traffic flow analysis and simulation studies. The dataset and related resources are publicly accessible at https://github.com/AIxMobility/The-DRIFT.