摘要
为了精确预测城市轨道交通设备故障等突发事件致使的列车延误时间,提升应急处置效率和乘客引导服务水平,对地铁突发事件互联网发布数据和现场事故数据进行了关联融合,对面向不平衡的地铁事故数据随机欠采样,提出了一种基于GBDT(梯度提升决策树)的级联分类预测方法,对地铁突发事件的延误时间进行预测。结果表明,GBDT级联分类方法在延误时间容许偏差为0~5 min时的预测延误时间准确率,比现场发布的预测延误时间准确率高20%~25%,比GBDT多分类预测方法准确率高5%。
To accurately predict train the delay time caused by accidents such as urban rail transit equipment failure,and improve the emergency response disposal efficiency and the passenger guidance service level,the association fusion of internet data and field data of metro accidents is carried out.According to the unbalanced metro accident random undersampling data,a cascade classification prediction method based on GBDT(gradient boosting decision tree)is proposed to predict the delay time of metro accidents.The results indicate that when the delay time allowable deviation of GBDT cascade classification method is 0~5 min,the predicted delay time accuracy of the method is 20%~25%higher than that released on site,and 5%higher than that of GBDT multi-classification prediction method.
作者
欧冬秀
张馨尹
赵源
张雷
高博文
吴宇森
OU Dongxiu;ZHANG Xinyin;ZHAO Yuan;ZHANG Lei;GAO Bowen;WU Yusen(不详;School of Transportation Engineering,Tongji University,201804,Shanghai,China)
出处
《城市轨道交通研究》
北大核心
2022年第10期65-70,共6页
Urban Mass Transit
基金
国家重点研发计划项目(2018YFB1201403)。
关键词
城市轨道交通
列车
突发事件
延误时间预测
级联分类方法
梯度提升决策树
urban rail transit
train
emergency accident
delay time prediction
cascade classification method
GBDT(gradient boosting decision tree)
作者简介
第一作者/通信作者:欧冬秀,教授。
