Road Safety Risk Assessment Approach for Freight Vehicles Using Warning Data - Cheng Yang; Xiaoling Zhai; Xiaoqin Zhou; Tao Wang; Shiyi Chen; Xiyuan Zhang (IEEE Access, 2025)
Road transport is a vital pillar of the national economy. However, freight vehicles face significant safety challenges during operation due to characteristics such as heavy-load transportation, long-distance travel, and complex cargo types. Traditional accident data analysis methods rely on historical accident statistics, which suffer from strong lagging effects and difficulties in achieving proactive risk identification. To address this, this paper proposes a real-time early-warning-data-based approach for road risk assessment. First, the global Moran’s I index is employed to analyze the spatial clustering characteristics of warning points. Second, an evaluation system is constructed, with the relative occurrence rates of improper driving behavior and abnormal vehicle status warnings as core indicators. The entropy weight method is applied to determine the weights of each indicator, enabling the quantitative calculation of road segment risk values. Finally, cluster analysis is used to determine optimal risk classification thresholds. The method is validated using warning data and historical accident records from 47 road segments across three major roads in Nanning. The results demonstrate that spatial clusters of warning points strongly correlate with high-accident-frequency road segments. The risk classification thresholds exhibit excellent discriminative performance, with boundary values of 0.038 (94.44% accuracy) between low- and medium-risk segments and 0.075 (96.00% accuracy) between medium- and high-risk segments. Significant differences in accident occurrence rates are observed across risk levels: high-risk segments average 7.73 accidents, far exceeding medium-risk (3.53) and low-risk segments (1.35). This study confirms the efficacy of early-warning data in risk assessment, providing transportation authorities with a data-driven risk management tool. The proposed method offers a novel approach for proactive safety control in freight vehicle transportation.