Swarm intelligence freeway–urban trajectories (SWIFTraj) dataset—Part II: Graph-based approach for trajectory connection

In Part I of this companion study series, we introduced swarm intelligence freeway–urban trajectories (SWIFTraj), a new open-source vehicle trajectory dataset collected using an unmanned aerial vehicle (UAV) swarm. The dataset has two distinctive features. First, by connecting trajectories across consecutive UAV videos, it provides long-distance continuous trajectories, with the longest exceeding 4.5 km. Second, it covers an integrated traffic network consisting of both freeways and their connected urban roads. However, obtaining such long-distance continuous trajectories from a UAV swarm is challenging, due to the need for accurate time alignment across multiple videos and the irregular spatial distribution of the UAVs. To address these challenges, this study proposes a novel graph-based approach for connecting vehicle trajectories captured by a UAV swarm. An undirected graph is constructed to represent flexible UAV layouts, and an automatic time alignment method based on trajectory matching cost minimization is developed to estimate optimal time offsets across videos. To associate trajectories of the same vehicle observed in different videos, a vehicle matching table is established using the Hungarian algorithm. The proposed approach is evaluated using both real-world and simulated data. The results from real-world experiments show that the time alignment error is within three video frames, corresponding to approximately 0.1 s, and that vehicle matching achieves a consistently high F1-score. These findings demonstrate the effectiveness of the proposed method in addressing key challenges in UAV-based trajectory connection and highlight its potential for large-scale vehicle trajectory collection.