摘要
为提升风险等级判别的准确性,破解城市轨道交通系统风险实时管控和事件应急处理的关键问题,构建了改进的特征选择算法(Im-F-score+XGB)对突发事件风险因素的特征进行筛选,通过分析城市轨道交通突发事件的基础数据,计算各风险特征的重要度,挖掘不同特征对突发事件风险等级的影响程度,得到突发事件风险等级判定的重要特征;同时,采用多时间窗循环扫描方法和加权级联残差森林模型相融合的思路,获得突发事件风险等级与风险特征的映射关系,建立了改进的突发事件风险等级判别模型(Im-F-GCF)。通过与RF、HGBDT、GCF、LightGBM 4个代表模型对比分析,显示出本文提出的Im-F-GCF模型的有效性。
In order to promote the accuracy of risk level identification,to solve the real-time risk control and emergency treatment problems of urban rail transit system,an improved feature selection algorithm(Im-F-score+XGB)on filtering features of the risk factors for emergencies was proposed.Through the analysis of the basic data of urban rail transit emergency,the feature importance degree of each risk feature was calculated,the influence degree of different features on the risk of emergencies was excavated,and the important features for determining the risk level of emergencies was obtained.Besides,the cyclic multi-time window scanning method and the weighted cascade residual forest model were used to obtain the mapping relationship between the emergency risk grade and the feature of risk features,and an improved emergency risk level identification model(Im-F-GCF)was established.Compared with RF,HGBDT,GCF and LightGBM models,the validity of the proposed model is verified.
作者
范博松
邵春福
FAN Bo-song;SHAO Chun-fu(School of Traffic Management,People's Public Security University of China,Beijing 100038,China;Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport,Beijing Jiaotong University,Beijing 100044,China;School of Transportation Engineering,Xinjiang University,Urumqi 830046,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第2期427-435,共9页
Journal of Jilin University:Engineering and Technology Edition
基金
国家重点研发计划“交通载运装备与智能交通技术”重点专项项目(SQ2023YFB4300098)
国家自然科学基金项目(52072025)。
