|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 ZhenJi GenlinTang Mengmeng
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
trajectory outlier detectionregion of interestclusteringMapReduce
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.

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Last Update: 2019-03-30