|Table of Contents|

An Algorithm for Detecting Anomalous Trajectory BetweenInterest Regions Based on Clustering(PDF)

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

Issue:
2019年01期
Page:
59-
Research Field:
·人工智能算法与应用专栏·
Publishing date:

Info

Title:
An Algorithm for Detecting Anomalous Trajectory BetweenInterest Regions Based on Clustering
Author(s):
Xu Zhen; Ji Genlin; Tang Mengmeng
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
trajectory outlier detection; region of interest; clustering; MapReduce
PACS:
TP39
DOI:
10.3969/j.issn.1001-4616.2019.01.010
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.

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.

Memo

Memo:
-
Last Update: 2019-03-30