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

Abstract

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

CLC Number:

U495

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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References 22

1	Byungtae C, Lee S H. A study on intelligent traffic system related with smart city[J]. International Journal of Smart Home, 2015, 9(7): 223-230.
2	Zhou R G, Zhong L D, Zhao N L, et al. The development and practice of china highway capacity research[J]. Transportation Research Procedia, 2016, 15: 14-25.
3	赵怀鑫, 邓然然, 张英杰, 等. 一种用于高速公路通行情况分析的收费数据挖掘方法[J]. 中国公路学报, 2018, 31(8): 155-164.
	Zhao Huai-xin, Deng Ran-ran, Zhang Ying-jie, et al. A toll data mining method for expressway traffic situation analysis[J]. China Highway Journal, 2018, 31(8): 155-164.
4	Swapna S, Niranjan P, Srinivas B, et al. Data cleaning for data quality[C]∥2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2016: 344-348.
5	Yoon K, Bae D. Pattern-based outlier detection method identifying abnormal attributes in software project data[J]. Information & Software Technology, 2010, 52(2): 137-151.
6	Juhola M, Joutsijoki H, Aalto H, et al. On classification in the case of a medical data set with a complicated distribution[J]. Applied Computing & Informatics, 2014, 10(1/2): 52-67.
7	Greenwood N, Shields K. An introduction to data cleaning using internet search data[J]. Australian Economic Review, 2017, 50(3): 363-372.
8	Dilling S, Macvicar B. Cleaning high-frequency velocity profile data with autoregressive moving average (ARMA) models[J]. Flow Measurement & Instrumentation, 2017, 54: 68-81.
9	Titouna C, Naït-abdesselam F, Khokhar A. A novel data cleansing approach for sensitive applications of wireless sensor networks[C]∥2019 International Conference on Smart Applications, Communications and Networking (SmartNets), Sharm El Sheikh, Egypt, 2019: 1-6.
10	肖心园, 江冰, 任其文,等. 基于插值法和皮尔逊相关的光伏数据清洗[J]. 信息技术, 2019, 43(5): 19-22, 28.
	Xiao Xin-yuan, Jiang Bing, Ren Qi-wen, et al. Photovoltaic data Cleaning based on interpolation and Pearson correlation [J]. Information Technology, 2019, 43(5):19-22, 28.
11	苗润华. 基于聚类和孤立点检测的数据预处理方法的研究[D]. 北京:北京交通大学计算机与信息技术学院, 2012.
	Miao Run-hua. Research on data preprocessing Method based on clustering and outlier detection[D]. Beijing: Beijing Jiaotong University College of Computer and Information Technology, 2012.
12	封富君, 姚俊萍, 李新社, 等. 大数据环境下的数据清洗框架研究[J]. 软件, 2017, 38(12): 193-196.
	Feng Fu-jun, Yao Jun-ping, Li Xin-she, et al. Research on data cleaning framework in big data environment[J]. Software, 2017, 38(12): 193-196.
13	Pappas C, Papalexiou S, Koutsoyiannis D. A quick gap filling of missing hydrometeorological data[J]. Journal of Geophysical Research Atmospheres, 2015, 119(15): 9290-9300.
14	Pilsung K. Locally linear reconstruction based missing value imputation for supervised learning[J]. Neurocomputing, 2013, 118: 65-78.
15	Zhao L, Chen Z K, Yang Z N, et al. Local similarity imputation based on fast clustering for incomplete data in cyber-physical systems[J]. IEEE Systems Journal, 2016, 12(2): 1610-1620.
16	邹嵩涵. 面向高速公路收费数据的异常行为分析与应用[D]. 成都: 电子科技大学计算机科学与技术学院, 2020.
	Zou Song-han. Analysis and application of abnormal behavior oriented expressway toll data[D]. Chengdu: College of Computer Science and Technology,University of Electronic Science and Technology, 2020.
17	周舟. 高速公路异常数据检测方法研究[D]. 长春: 长春理工大学计算机技术学院, 2018.
	Zhou Zhou. Research on highway abnormal data detection method [D]. Changchun: College of Computer Technology, Changchun University of Science and Technology,2018.
18	蒋怡玥. 基于高速公路收费数据的交通分布时空相关性研究[D]. 北京: 北京交通大学交通运输学院, 2019.
	Jiang Yi-yue. Research on the spatio-temporal correlation of traffic distribution based on freeway toll data[D]. Beijing: College of Transportation, Beijing Jiaotong University, 2019.
19	Pei Li-li, Sun Zhao-yun, Han Yu-xi, et al. Highway event detection algorithm based on improved fast peak clustering[J]. Mathematical Problems in Engineering, 2021(1): 1-13.
20	李松松. 基于收费数据挖掘的高速公路旅行时间预测和交通状态判别应用研究[D]. 广东: 华南理工大学土木与交通学院, 2017.
	Li Song-song. Application research on highway travel time prediction and traffic state discrimination based on toll data mining [D]. Guangdong: College of Civil Engineering and Transportation,South China University of Technology, 2017.
21	Mohamad I, Usman D. Standardization and its effects on K-means clustering algorithm[J]. Research Journal of Applied Sciences, Engineering and Technology, 2013, 6(17): 3299-3303.
22	Pei L L, Sun Z Y, Yu T, et al. Pavement aggregate shape classification based on extreme gradient boosting[J]. Construction and Building Materials, 2020, 256: No. 119356.

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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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Algorithm for repairing abnormal toll data of expressway based on SSC and XGBoost

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