[1]许 振,吉根林,唐梦梦.基于聚类的兴趣区域间异常轨迹并行检测算法[J].南京师范大学学报(自然科学版),2019,42(01):59.[doi:10.3969/j.issn.1001-4616.2019.01.010]
　Xu Zhen,Ji Genlin,Tang Mengmeng.An Algorithm for Detecting Anomalous Trajectory BetweenInterest Regions Based on Clustering[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(01):59.[doi:10.3969/j.issn.1001-4616.2019.01.010]
点击复制

基于聚类的兴趣区域间异常轨迹并行检测算法()
分享到：

《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年01期
页码:
59
栏目:
·人工智能算法与应用专栏·
出版日期:
2019-03-20

文章信息/Info

Title:
An Algorithm for Detecting Anomalous Trajectory BetweenInterest Regions Based on Clustering
文章编号:
1001-4616(2019)01-0059-06
作者:
许 振; 吉根林; 唐梦梦
南京师范大学计算机科学与技术学院,江苏 南京 210023
Author(s):
Xu Zhen; Ji Genlin; Tang Mengmeng
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
关键词:
异常轨迹检测; 兴趣区域; 聚类; MapReduce
Keywords:
trajectory outlier detection; region of interest; clustering; MapReduce
分类号:
TP39
DOI:
10.3969/j.issn.1001-4616.2019.01.010
文献标志码:
A
摘要:
轨迹异常检测能够用来分析移动对象的异常运动行为,在交通运输、医疗监护等领域都有广泛应用. 兴趣区域是移动对象集中活动的区域. 本文提出了一种新的兴趣区域间异常轨迹检测算法(Detecting Anomalous Trajectories Between Interest Regions,DATIR). 不同于已有的从局部采样点进行检测的算法,DATIR算法综合考虑了轨迹的局部特征和全局特征,利用聚类方法检测兴趣区域间的异常轨迹,并能挖掘出兴趣区域间的正常路径. 为了提高海量轨迹数据的异常检测效率,在DATIR算法的基础上,提出了一种并行检测算法(Parallel Algorithm for Detecting Anomalous Trajectories Between Interest Regions,PDATIR). 实验结果表明,DATIR算法能够有效地检测兴趣区域间的异常轨迹,并且能够检测出兴趣区域间的正常轨迹; PDATIR算法在大数据集上表现出了明显的性能优势,具有较好的可扩展性和较高的加速比.
Abstract:
Trajectory anomaly detection can be used to analyze the anomalous behavior mobile objects,and it has been widely used in transportation,medical monitoring and other fields. Interest region is an active activity area. They can be railway stations,airports,schools,shopping malls and so on. A new algorithm for detecting trajectory outliers between interest regions,called DATIR(Detecting Anomalous Trajectories between Interest Regions),is proposed. Unlike the existing algorithm to detect from the local sample points,algorithm DATIR takes into account the local and global characteristics of the trajectory,and uses clustering method to detect the normal path and anomalous trajectory between interest regions. Algorithm DATIR has a wide range of application areas,such as taxi fraud monitoring,road planning,and so on. In order to improve the efficiency of mining trajectory from massive trajectory datasets,the parallel algorithm for detecting trajectory outliers based on MapReduce framework,which is called PDATIR(Parallel algorithm for Detecting Anomalous Trajectories between Interest Regions),is presented. The experimental results demonstrate that algorithm DATIR can effectively detect the anomalous trajectories between regions of interest,and can mine the normal path. Algorithm PDATIR shows obvious performance advantages over large data sets,and it has the high scalability and good speedup ratio.

参考文献/References:

[1] 吉根林,赵斌. 时空轨迹大数据模式挖掘研究进展[J]. 数据采集与处理,2015,30(1):47-58.
[2]刘大有,陈慧灵,齐红,等. 时空数据挖掘研究进展[J]. 计算机研究与发展,2013,50(2):225-239.
[3]GIANNOTTI F,NANNI M,PINELLI F,et al. Trajectory pattern mining[C]//Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. California,USA:ACM,2007:330-339.
[4]ZHENG Y. Trajectory data mining:an overview[J]. ACM transactions on intelligent systems and technology(TIST),2015,6(3):29.
[5]GUPTA M,GAO J,AGGARWAL C,et al. Outlier detection for temporal data[J]. Synthesis lectures on data mining and knowledge discovery,2014,5(1):1-129.
[6]姜金凤. 移动对象轨道异常检测算法的研究[D]. 南京:南京航空航天大学,2010:1-19.
[7]安计勇,朱猛,翟靖轩,等. 轨迹多因素异常集成检测[J]. 计算机工程与设计,2015,36(10):2700-2705.
[8]LEE J G,HAN J,LI X. Trajectory outlier detection:a partition-and-detect framework[C]//Proceedings of the 24th International Conference on Data Engineering(ICDE). Cancún,México:IEEE,2008:140-149.
[9]刘良旭,乔少杰,刘宾,等. 基于 R-Tree的高效异常轨迹检测算法[J]. 软件学报,2009,20(9):2426-2435.
[10]ZHANG D,LI N,ZHOU Z H,et al. iBAT:detecting anomalous taxi trajectories from GPS traces[C]//Proceedings of the 13th international conference on Ubiquitous computing. Beijing,China:ACM,2011:99-108.
[11]GE Y,XIONG H,ZHOU Z,et al. Top-eye:Top-k evolving trajectory outlier detection[C]//Proceedings of the 19th ACM international conference on Information and knowledge management. Toronto,Canada:ACM,2010:1733-1736.
[12]FONTES V C,de ALENCAR L A,RENSO C,et al. Discovering trajectory outliers between regions of interest[C]//GeoInfo 2013:Proceedings of the XIV Brazilian Symposium on Geoinformastics. Jord(～overa)o,S(～overa)o Paulo,Brazil:GeoInfo,2013:49-60.
[13]ZHANG Z,HUANG K,TAN T. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes[C]//Proceedings of the 18th International Conference on Pattern Recognition(ICPR). Hong Kong:IEEE,2006,3:1135-1138.
[14]WANG Y,LEI P,ZHOU H,et al. Using DTW to measure trajectory distance in grid space[C]//Proceedings of the 4th IEEE International Conference on Information on Science and Technology(ICIST). Shenzhen,China:IEEE,2014:152-155.
[15]数据堂. 某北方城市12000辆出租车GPS位置数据(2012年11月)[DB/OL]. [2013-9-16]. http://www.datatang.com/data/44502.

备注/Memo

备注/Memo:
收稿日期:2018-06-16.
基金项目:国家自然科学基金(41471371).
通讯联系人:吉根林,博士,教授,博士生导师,研究方向:数据挖掘与大数据分析技术. E-mail:glji@njnu.edu.cn
更新日期/Last Update: 2019-03-30