基于SSC与XGBoost的高速公路异常收费数据修复算法

doi:10.13229/j.cnki.jdxbgxb20210231

摘要/Abstract

摘要：

针对高速公路收费数据中的异常检测和修复问题，分别了提出了基于相似系数和SSC（Sum of similar coefficients）的异常检测算法以及基于XGBoost （eXtreme gradient boosting）的多维数据预测修复方法，并使用这两种算法对实际收费数据进行了异常检测和修复处理。结果表明，基于SSC的异常检测算法能够考虑到数据维度之间的相关性，准确地对多维数据异常检测；同时XGBoost多元预测算法与仅针对单维数据的改进拉格朗日算法相比，R²从0.9166提升至0.9856。本文算法有效而准确，能够为公路管理部门数据分析提供高质量的数据支持。

关键词: 交通信息工程, 异常检测与修复, 相似系数和, XGBoost, K?means

Abstract:

For the detection and repair of anomaly in expressway toll data， an anomaly detection algorithm based on SSC （Sum of Similar Coefficients） suitable for multi-dimensional reading data joint detection and an anomaly repair algorithm based on XGBoost （eXtreme Gradient Boosting） multidimensional data prediction repair method are proposed，and above methods are applied to data test. The results show that the SSC considers the correlation between data dimensions and could accurately detect anomaly in multi-dimensional data. Meanwhile， compared to the improved Lagrange interpolation， the R² of the proposed method increased from 0.9166 to 0.9856. The algorithms proposed in this paper are effective and could provide high quality data support for data analysis and statistics of expressway management departments.

Key words: traffic information engineering, anomaly detection and repair, sum of similar coefficients, XGBoost, K-means

中图分类号:

U495

裴莉莉,孙朝云,韩雨希,李伟,户媛姣. 基于SSC与XGBoost的高速公路异常收费数据修复算法[J]. 吉林大学学报(工学版), 2022, 52(10): 2325-2332.

Li-li PEI,Zhao-yun SUN,Yu-xi HAN,Wei LI,Yuan-jiao HU. Algorithm for repairing abnormal toll data of expressway based on SSC and XGBoost[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(10): 2325-2332.

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特征名称	含义	单位	样例数据
ID	数据序号	/	50968476
InTime	进站口时间	/	2016/10/26 09：46：50
OutTime	出站口时间	/	6：28
InStation Name	进站名称	/	15
OutStation Name	出站名称	/	16
InLoad	进站车辆总重（进站荷载）	100 kg	40
OutLoad	出站车辆总重（出站荷载）	100 kg	40
Credit	消费金额	元	555.75
Last Balance	消费后余额	元	2345.5

Sort	OutLoad/100 kg	OutStation Name	InLoad/100 kg	InStation Name
总数	866.00	879.00	869.00	879.00
平均值	39.4942	4.7610	39.33	4.54
标准差	21.5662	3.9037	21.63	3.73
最小值	10.0000	1.0000	1.00	0.00
25%	25.0000	2.0000	25.00	2.000000
50%	35.0000	3.0000	35.00	3.000000
75%	46.0000	7.0000	46.00	6.000000
最大值	1000.0000	16.0000	83.00	20.000000
总数	866.00	879.00	869.00	879.00

模型参数	参数解释
n_estimation	最佳迭代次数
max_depth min_child_weight	最大深度最小叶子节点样本权重和
subsample	随机采样数
colsample bytree	每棵随机采样列数占比

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