Identifying accident black spots based on the accident spacing distribution

Abstract

The identification of accident black spots is of great significance for the prevention of traffic accidents. Commonly used accident black spot identification methods divide road sections for the analysis of accident data, the direct result of which is the splitting of accident black spots, which affects the results. This paper is based on three years of traffic accident data from the Beijing–Harbin Expressway, including the time and location of traffic accidents, form of the accident fatalities, severe injuries, slight injuries, and property damage only (PDO). To avoid road division effects, an identification method based on the accident spacing distribution is established by using quality control theory. The results show that the average number of accidents per kilometer by the method proposed in this paper is 42, which is much higher than 10, identified by other identification methods. The method proposed in this paper improves the accuracy of the identification results. This method avoids the problem of road segmentation found in other common methods and can more accurately reflect the spatial distribution of traffic accidents. Thus making the identification of accidents more scientific and accurate. • Reviews and summarizes the identification methods of accident black spots. • Uses the Poisson distribution to avoid the division of road sections. • Introduces the concept of quality control management –3 σ principle provide basis for the method. • Compared with other methods, the accuracy of the method proposed in the paper is higher.