In Part I of this companion paper series, we introduced SWIFTraj, an open-source vehicle trajectory dataset collected by a UAV swarm. It provides long-distance continuous trajectories by connecting vehicle trajectories across consecutive UAV videos, with the longest trajectory exceeding 4.5 km, and covers an integrated network of freeways and connected urban roads. However, trajectory connection in UAV swarms is challenging because of video time-offset errors and irregular UAV layouts. To address these issues, this paper proposes a graph-based trajectory connection method. An undirected graph is used to represent flexible UAV layouts, an automatic time-alignment method is developed by minimizing trajectory matching costs, and cross-video vehicle association is performed using a Hungarian-algorithm-based matching table. Experiments on real-world and simulated data show that the proposed method achieves time alignment errors within three frames, about 0.1 s, and consistently high vehicle-matching F1-scores.

This paper presents a detailed description and characterization of a new open-source vehicle trajectory dataset, namely SWIFTraj, constructed from videos recorded by a swarm of 16 drones equipped with 5.4K-resolution cameras. The dataset is distinguished from existing open-source trajectory datasets in several aspects. First, it provides long-distance continuous trajectories of up to 4.5 km on a freeway, enabling in-depth investigation of traffic phenomena and their spatial and temporal evolution. Second, the data collection site covers an integrated network consisting of a long freeway corridor and parts of its connected urban network, facilitating traffic analysis and modeling from a network perspective. The dataset is publicly available at the SWIFTraj website (https://www.swiftraj.com). 

The package supports reproduction of the main numerical results reported in the study, including the MATLAB/Simulink simulations of a heterogeneous five-truck platoon, the longitudinal model predictive control with bounded RBF residual compensation, the LLM-guided scenario interpretation and deterministic validation process, and the scenario-adaptive lateral organization optimization.

The package includes MATLAB/Simulink model files, MATLAB scripts for lateral organization optimization and figure generation, Python scripts for LLM-guided scenario interpretation and safety validation, scenario input and output files, trained RBF model files, processed simulation data, and reference output figures/tables. The simulated scenarios are generic road and traffic segments and are not based on proprietary, confidential, or human-subject data. Detailed instructions for reproducing the main results are provided in the README file and the replication explanatory file.

The simulated scenarios are generic road and traffic segments and are not based on proprietary, confidential, or human-subject data.

The replication package contains processed real-world emission datasets and Python scripts used to reproduce the main analyses and figures presented in this study. The datasets include overall trip-average emissions, operating mode-specific emission results, and SCR upstream/downstream NOx data for China V and China VI heavy-duty diesel trucks tested using PEMS under real-road conditions. All analyses were conducted using Python 3.9 in PyCharm Community Edition 2023.1. Due to confidentiality restrictions, only processed and aggregated datasets are provided.