Identification Method for Crash-Prone Sections of Mountain Highway under Complex Weather Conditions

Abstract

Mountain highway crashes usually have a weather tendency, and the crash-prone sections show obvious weather differences. However, there were few targeted quantitative analyses of the impact of weather conditions on crash-prone sections in previous studies. Aiming at the problem that traditional identification methods ignore the difference in weather, this paper proposed the time-spatial density ratio method. The method quantified the length of the road section, the period, and the influence of different weather conditions through the time-spatial density ratio. Then the time-spatial density ratios under different weather conditions were comprehensively sorted in parallel. Finally, the risk threshold was determined according to the characteristics of the cumulative frequency curve’s double inflection points, and the crash-prone sections under each weather condition were identified. This paper evaluated the crash-prone sections of the G76 Expressway. Moreover, the crash risk situation under each weather condition was characterized through kernel density analysis. The method was compared with the cumulative frequency method, a traditional method suitable for Chinese highways with similar application conditions. The effective search index was utilized as a comparison factor. The results showed that the effective search index of the time-spatial density ratio method was more than 80% greater than that of the cumulative frequency method.