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.

This study develops a crash risk prediction model at the individual vehicle level by leveraging the reasoning capability of large language models (LLMs). Instead of using the LLM itself for online prediction, the proposed framework converts LLM reasoning into structured supervision and distills it into a lightweight temporal graph network, which is then calibrated with real crash data. The framework is evaluated using pre-crash vehicle trajectories from 109 real-world crashes captured in multi-year drone videos at freeway merging and weaving segments in Nanjing, China. Results show that the proposed model outperforms representative benchmarks, including direct LLM inference, direct supervised training on real crash data, and traditional surrogate safety measures. Compared with direct supervised training using the same student architecture, it increases AUC by 7.8% and reduces MAE by 28.0%. Moreover, the proposed model is particularly effective when real crash data are limited. When only 10% of the fine-tuning data is used, it achieves the best performance among the compared methods, reducing MAE by 19.6% relative to the LLM baseline and by 38.0% relative to direct supervised training. These findings suggest that the proposed framework can improve prediction performance and data efficiency when real crash data are limited.