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The identification of accident-prone locations on roads remains a crucial area of focus in road safety research. Among the various approaches used, the cumulative frequency curve method stands out as one of the most widely employed techniques for this purpose. However, it is important to address the limitations of the traditional cumulative frequency curve method and propose enhancements. In this article, we present an improved version of the method that builds upon the foundation of the traditional approach. Our enhanced method introduces the concept of equivalent accident numbers, which enables us to gauge the severity of different accidents more effectively. By utilizing the sliding window method, we divide road sections into manageable segments and construct cumulative frequency curves based on equivalent accident numbers per kilometer. To determine critical values for identifying accident-prone and potential accident-prone locations, we examine the equivalent accident numbers at specific cumulative frequencies, namely 80% and 95%. These critical values, denoted as N80 and N95, serve as benchmarks for our analysis. An empirical validation was conducted using historical traffic accident data from the Guizhou Section of the G75 Lanzhou-Haikou Expressway. The results of the analysis using the traditional cumulative frequency curve method revealed accident-prone sections accounting for 9.67% of the total length and 40.17% of the accident counts. However, our improved method identified accident-prone sections representing only 3.78% of the total length but capturing 20.06% of the accident counts. Furthermore, we successfully identified potential accident-prone locations spanning 11.03% of the total length and accounting for 33.95% of the accident counts. Compared to the traditional algorithm, our enhanced approach demonstrates superior accuracy and precision by pinpointing more accident-prone locations and potential accident-prone locations. These findings hold significant implications for the graded management of road safety, ultimately leading to improved road safety outcomes.
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