An improved density peaks clustering algorithm based on k nearest neighbors and turning point for evaluating the severity of railway accidents. (May 2023) - Shi, Lingyuan; Yang, Xin; Chang, Ximing; Wu, Jianjun; Sun, Huijun
Highlights: The paper provides a reference for the hierarchical response to railway accidents. Scenario-response mode is integrated into the dynamic grading of railway accidents. An improved DPC algorithm is present for dynamical evaluation of accident severity. Experiments are conducted on the real-world case of railway accidents in the UK. Abstract: The timely and scientific assessment of railway accident severity can provide effective support for making a suitable rescue plan. This paper proposes an improved density peaks clustering algorithm based on scenario construction for dynamically tackling the severity ranking problem of railway accidents. Firstly, the scenario is introduced into the selection of grading indicators of railway accidents, and a five-dimensional railway accident scenario expression model based on knowledge element is proposed to dynamically select and express the grading indicators. Secondly, an improved density peaks clustering algorithm based on k nearest neighbors and turning point (IDPC-KNN-TP) as the grading model of railway accidents is established, which optimizes the local density measurement, the selection of cluster center points, and the assignment strategy of density peaks clustering (DPC). Finally, the railway accidents from 2010 to 2021 in the UK as a case study are analyzed. The results show that the performance of IDPC-KNN-TP is better than others, and the grading algorithm in this paper has good practical performance. The dynamic grading strategy and the emergency disposal suggestions are put forward to provide a reference for the hierarchical response to railway accidents.