[1]陈淑燕,王炜,瞿高峰.服务于智能交通系统的离群交通数据识别[J].东南大学学报(自然科学版),2008,38(4):723-726.[doi:10.3969/j.issn.1001-0505.2008.04.035]
 Chen Shuyan,Wang Wei,Qu Gaofeng.Outlier detection in traffic data sets serving for intelligent transportation system[J].Journal of Southeast University (Natural Science Edition),2008,38(4):723-726.[doi:10.3969/j.issn.1001-0505.2008.04.035]
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服务于智能交通系统的离群交通数据识别()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
38
期数:
2008年第4期
页码:
723-726
栏目:
交通运输工程
出版日期:
2008-07-20

文章信息/Info

Title:
Outlier detection in traffic data sets serving for intelligent transportation system
作者:
陈淑燕12 王炜1 瞿高峰2
1 东南大学交通学院, 南京 210096; 2 南京师范大学江苏省光电重点实验室, 南京 210097
Author(s):
Chen Shuyan12 Wang Wei1 Qu Gaofeng2
1 School of Transportation, Southeast University, Nanjing 210096, China
2 Key Laboratory of Optoelectronics of Jiangsu Province, Nanjing Normal University, Nanjing 210097,China
关键词:
交通数据 离群数据挖掘 识别 基于统计的方法 基于距离的方法 基于密度的方法
Keywords:
traffic data outlier mining detection statistic-based distance-based density-based
分类号:
U491.14
DOI:
10.3969/j.issn.1001-0505.2008.04.035
摘要:
为了提高交通建模的准确性和可靠性, 或者提取重要的有价值的隐藏信息,将离群数据挖掘技术引入交通数据处理.首先分析了3种典型的离群数据挖掘算法:基于统计的方法、基于距离的方法以及基于密度的方法的原理、特点和时间复杂性; 其次给出了2个实例分析,一是在建立交通流量预测模型前,将基于统计的方法和基于距离的离群检测方法分别用于交通量时间序列,寻找离群数据; 二是将基于距离的方法和基于密度的方法用于路面平整度检测.实例研究表明,离群数据挖掘算法可有效识别异常交通数据,在交通工程领域具有较大的应用潜力.
Abstract:
Outlier mining technique is introduced into traffic data process to detect and analyze the outliers of traffic data sets in order to improve the veracity and reliability of traffic modeling, or draw out important and valuable hidden information. First, the principle, characteristic and time complexity of three typical outlier mining approaches, that is statistical-based approach, distance-based approach, density-based approach, are analyzed. Two examples are then given. One of them is to detect outliers hidden in traffic volume before prediction model built based on statistical-based and distance-based outlier mining approaches, the other applies statistical-based and distance-based outlier mining approaches to pavement roughness detection. The experimental results illustrate that these outliers mining methods are feasible and valid to detect outliers in traffic data sets, and have a good potential of application in traffic engineering.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介: 陈淑燕(1967—),女,博士,副教授; 王炜(联系人),男,博士,教授,博士生导师,wangwei@seu.edu.cn.
基金项目: 国家重点基础研究发展计划(973计划)资助项目(2006CB705500)、中国博士后科学基金资助项目(20070411016)、江苏省博士后科研资助计划项目(2007).
引文格式: 陈淑燕,王炜,瞿高峰.服务于智能交通系统的离群交通数据识别[J].东南大学学报:自然科学版,2008,38(4):723-726.
更新日期/Last Update: 2008-07-20