DRIFT open dataset: A drone-derived intelligence for traffic analysis in urban environment
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912346886111232 |
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| author | Lee, Hyejin Hong, Seokjun Song, Jeonghoon Cho, Haechan Jin, Zhixiong Kim, Byeonghun Jin, Joobin Im, Jaegyun Noh, Byeongjoon Yeo, Hwasoo |
| author_facet | Lee, Hyejin Hong, Seokjun Song, Jeonghoon Cho, Haechan Jin, Zhixiong Kim, Byeonghun Jin, Joobin Im, Jaegyun Noh, Byeongjoon Yeo, Hwasoo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DRIFT open dataset: A drone-derived intelligence for traffic analysis in urban environment Lee, Hyejin Hong, Seokjun Song, Jeonghoon Cho, Haechan Jin, Zhixiong Kim, Byeonghun Jin, Joobin Im, Jaegyun Noh, Byeongjoon Yeo, Hwasoo Computer Vision and Pattern Recognition I.2.10; I.4.8; H.2.8; J.7 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. |
| title | DRIFT open dataset: A drone-derived intelligence for traffic analysis in urban environment |
| topic | Computer Vision and Pattern Recognition I.2.10; I.4.8; H.2.8; J.7 |
| url | https://arxiv.org/abs/2504.11019 |