Clustering-Based Trajectory Outlier Detection

The improvement in mobile computing techniques has generated massive trajectory data, which represent the mobility of moving objects like vehicles, animals, and people. Mining trajectory data and especially outlier detection in trajectory data is an attractive and challenging topic that fascinated m...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2020, Vol.11 (5)
Main Authors: Eldawy, Eman O., M.O., Hoda
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Computing time
Data analysis
Datasets
Mobile computing
Outliers (statistics)
Segments
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Summary:The improvement in mobile computing techniques has generated massive trajectory data, which represent the mobility of moving objects like vehicles, animals, and people. Mining trajectory data and especially outlier detection in trajectory data is an attractive and challenging topic that fascinated many researchers. In this paper, we propose a Clustering-Based Trajectory Outlier Detection algorithm (CB-TOD). The proposed algorithm partitions a trajectory into line segments and decreases those line segments to a smaller set (Summary-trajectory SS(t)) without affecting the spatial properties of the original trajectory. After that the CB-TOD algorithm using a clustering method to detect the cluster with the smallest number of segments for a trajectory and a small number of neighbors to be sub-trajectory outliers for this trajectory. Also, our proposed algorithm can detect outlier trajectories in the dataset. The main advantage of CB-TOD algorithm is reducing the computational time for outlier detection especially for big trajectory data without affecting the efficiency of the outlier detection results. Experimental results demonstrate that CB-TOD outperforms the state of art existing algorithms in identifying outlier sub-trajectories and also outlier trajectories in real trajectory dataset.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2020.0110520