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Open AccessArticle

Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

1
Department of Industrial Engineering, Pusan National University, Busan 46241, Korea
2
Samsung Heavy Industry, Geoje 13486, Korea
3
IOChord Inc., Busan 48059, Korea
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Pulshashi, I.R.; Bae, H.; Choi, H.; Mun, S. Smoothing of Trajectory Data Recorded in Harsh Environments and Detectioning of Outlying Trajectories. In Proceedings of the 7th International Conference of Emerging Databases: Technologies, Applications and Theory, Busan, South Korea, 7–9 August 2017; Springer: Singapore, 2018; pp. 89–98.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 272; https://doi.org/10.3390/ijgi8060272
Received: 29 April 2019 / Revised: 1 June 2019 / Accepted: 9 June 2019 / Published: 12 June 2019
(This article belongs to the Special Issue Spatial Data Science)
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Abstract

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach. View Full-Text
Keywords: trajectory preprocessing; trajectory data mining trajectory preprocessing; trajectory data mining
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Pulshashi, I.R.; Bae, H.; Choi, H.; Mun, S.; Sutrisnowati, R.A. Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments . ISPRS Int. J. Geo-Inf. 2019, 8, 272.

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