铁道运输与经济2025，Vol.47Issue(4)：152-161,10.DOI:10.16668/j.cnki.issn.1003-1421.2025.04.16
基于改进Apriori的地铁运维危险源致灾度量化及风险预判
Disaster-Causing Degree Quantification and Risk Prediction of Hazard Sources in Metro Operation and Maintenance Based on Improved Apriori
摘要
Abstract
To accurately predict the severity of metro operation accidents,this study proposed an ordered constraint Apriori-RF algorithm to quantify disaster degree levels of operation accidents.Firstly,a disaster-causing quantification model was constructed based on three dimensions:casualties,train delays,and facility damages.The K-means algorithm was clustered into four disaster degree levels.Secondly,the improved ordered constraint Apriori algorithm was introduced to explore nonlinear relationships between risks and disaster degree levels,yielding 42 effective association rules.Thirdly,these rules were input into a random forest algorithm for training,obtaining risk importance for disaster degree levels through Gini coefficient analysis.Finally,cases were verified and compared through the ordered constraint Apriori-RF method and random forest algorithm.Research demonstrates that the Apriori-RF method improves association rule mining effectiveness by 74.9%with higher efficiency.The results show a 14%reduction in the Root Mean Square Error(RMSE)and a 36%decrease in the Weighted Root Mean Square Error(WRMSE),indicating significantly higher accuracy.The research findings provide an accurate and effective method for quantitatively predicting disaster degree levels in metro operation accidents,holding theoretical significance and practical value in ensuring operation safety and disaster mitigation and prevention.关键词
地铁运维安全/风险管控/有序约束Apriori-RF方法/致灾度量化/风险预判Key words
Metro Operation and Maintenance Safety/Risk Management and Control/Ordered Constraint Apriori-RF Method/Disaster-Causing Degree Quantification/Risk Prediction分类
交通工程引用本文复制引用
唐永升,李花子..基于改进Apriori的地铁运维危险源致灾度量化及风险预判[J].铁道运输与经济,2025,47(4):152-161,10.基金项目
上海市哲学社会科学规划课题(2024BGL005) （2024BGL005）
