科技创新与应用2026，Vol.16Issue(12)：164-167,4.DOI:10.19981/j.CN23-1581/G3.2026.12.041
基于SOM与K-Means的铁路事故聚类可视化分析方法
余冠华1
作者信息
- 1. 中铁第四勘察设计院集团有限公司,武汉 430063
- 折叠
摘要
Abstract
To more clearly identify accident groups with high causal correlation and core accident-causing factors,and provide scientific references for various departments in the railway industry to formulate safety management and control strategies,thereby contributing to the systematic improvement of railway transportation safety levels.The SOM and K-Means clustering visualization analysis methods adopted in this paper can accurately identify and aggregate accident samples with similar accident-causing characteristics,and the clustering effect is significant and its effectiveness has been verified.In addition,through the construction of a railway accident topological distribution mapping model and a thermal map of accident-causing attributes,the visual presentation of railway accident clustering results has been completed.关键词
铁路事故/铁路致因/聚类/数据挖掘/可视化Key words
railway accident/railway cause/clustering/data mining/visualization分类
交通工程引用本文复制引用
余冠华..基于SOM与K-Means的铁路事故聚类可视化分析方法[J].科技创新与应用,2026,16(12):164-167,4.
