A Quantitative Method for Assessing Freeway Driving Risk Based on Continuous Observation Data
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Frequent freeway accidents cause significant casualties and economic losses, necessitating robust risk assessment methods. This study proposes a quantitative method for assessing freeway driving risk using continuous observational data from toll transactions. Based on toll data from the Yongguan Freeway in Guangdong Province, China (June–August 2022), 18 risk characteristic indicators for cargo vehicles and 13 for passenger vehicles were developed. Factor analysis reduced these indicators into five common factors, followed by K-means++ clustering to categorise vehicles into risk groups. The entropy weight method calculated risk scores, determining risk levels. The model identified 17.75% of cargo vehicles as high-risk and 14.03% as moderately high-risk, and 7.47% of passenger vehicles as high-risk and 1.08% as moderately high-risk. Validation using rescue events per 10,000 vehicles (RM) from a Guangdong Province accident database, due to limited crash data availability, confirmed consistency with model-assigned risk levels, supporting targeted safety interventions.
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Copyright (c) 2026 Chonghao Pang, Minping Gong; Chuhao Zhou, Qiang Zeng; Peiqun Lin
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