Cluster analysis of major expressway traffic accidents: Characteristics and influential factors

As China operates the world's most extensive expressway network, frequent traffic accidents undermine the sustainable development of its transportation system and negatively impact public travel safety. Variations in temporal and geographical conditions lead to differences in accident characteristics, distribution patterns, and contributing factors. This study aims to analyze the features, distribution, and influencing factors of expressway accidents in Guangdong Province from 2014 to 2023, and to propose evidence-based safety countermeasures.

A total of 131 expressway accidents recorded in Guangdong Province between 2014 and 2023 were statistically analyzed. Key accident characteristics, distribution patterns, and influencing factors were examined. Significant independent variables were standardized, and five determinant factors were extracted using factor analysis. These factors were subsequently classified into four distinct accident categories through K-means clustering to further explore underlying influences.

Since 2020, the number of expressway accidents in Guangdong Province increased substantially, averaging approximately 16 incidents annually. Accidents occurred more frequently in summer (29.77%) and spring (27.48%). Within a 24-h period, 34.35% of accidents took place during early morning hours, representing the highest proportion by time segment. Heavy vehicles (45.80%) and passenger cars (29.77%) were involved in the majority of accidents. Improper driving behavior was a significant contributing factor, accounting for 18.32% of incidents. Secondary accidents were found to result in considerably more severe consequences than primary accidents. A significant correlation was identified between expressway accidents and five determinant factors: environmental (F1), cause of accident (F2), vehicle-related (F3), road-related (F4), and temporal (F5). Influencing factors such as season, time period, expressway type, secondary accident occurrence, vehicle type distribution, and city were significant only within specific accident categories. In contrast, weather conditions, road surface status, visibility, and accident causes were significant across multiple categories.

The findings highlight distinct temporal, vehicular, and behavioral patterns associated with expressway accidents in Guangdong Province. The analysis of determinant factors and accident categories provides a nuanced understanding of contributing influences. Based on these results, targeted safety improvement strategies are proposed to support the development of expressway traffic safety measures and inform relevant policy-making.