基于梯度提升决策树级联分类方法的城市轨道
欧冬秀1,2张馨尹1赵源2,3张雷4高博文1吴宇森1
Urban Rail Transit Train Accident Delay Time Prediction Based on GBDT Cascade Classification Method
OU DongxiuZHANG XinyinZHAO YuanZHANG LeiGAO BowenWU Yusen
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作者信息:1.1. 同济大学交通运输工程学院， 201804， 上海；
2.2. 上海市轨道交通结构耐久与系统安全重点实验室， 201804， 上海；
3.3. 上海轨道交通运营管理中心， 200070， 上海；
4.4. 上海自主智能无人系统科学中心， 201210， 上海
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Affiliation:School of Transportation Engineering, Tongji University, 201804, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2022.10.013
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中图分类号/CLCN:U231.92
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栏目/Col:研究报告
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%。
Abstracts:
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.
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