Uncovering hidden risk groups among large trucks on freeways through driver behaviour profiling

With the rapid growth of freight transport in China, ensuring large truck safety on freeways is increasingly critical. However, existing studies focus on isolated behaviours or small datasets, lacking scalable methods to identify high-risk groups in real traffic flows. This study constructs 13 behavioural profiles for large trucks based on China's legal framework, covering speed patterns, load status, travel time and vehicle attributes. A Sample-Weighted Gaussian Mixture Model with Tsallis Entropy (SWGMM-T) is proposed to enhance clustering robustness and interpretability. Compared with four baseline models, SWGMM-T outperformed in clustering performance. Results show that cumulative overspeed travel time, overspeed distance, overspeed rates, abnormal stop rates and cumulative abnormal stop time are crucial in risk assessment. Crash rates per 10,000 trucks confirm this trend. Cluster 3 shows the highest crash rate and severe crash involvement, while Cluster 2 has the lowest crash rate and minimal severity. These findings suggest risks arise from combinations of risky behaviours.