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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
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Sensors and Materials, Volume 31, Number 11(4) (2019)
Copyright(C) MYU K.K.
pp. 3827-3834
S&M2052 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2019.2565
Published: November 30, 2019

Anomaly Detection in Taxi Flow by a Projection Method [PDF]

Myeong-Hun Jeong, Seung-Bae Jeon, Sangjun Park, and Sanggu Kang

(Received August 25, 2019; Accepted October 16, 2019)

Keywords: trajectory outliers, projection method, trajectory data mining, outlier detection

The prevalence of location-aware devices has propelled studies on the analysis of movement data. In this study, we investigated anomaly detection in taxi trajectories. A projection method was used to detect trajectory outliers. This is a robust statistical method and considers two dimensions of data simultaneously—distance and time. The experimental data included taxi movements in Seoul City and New York City. The results were compared with those of an alternative method, the Mahalanobis distance approach. The findings were observed to be similar. The proposed method can be used to improve the services of taxis and buses.

Corresponding author: Sangjun Park


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Cite this article
Myeong-Hun Jeong, Seung-Bae Jeon, Sangjun Park, and Sanggu Kang, Anomaly Detection in Taxi Flow by a Projection Method, Sens. Mater., Vol. 31, No. 11, 2019, p. 3827-3834.



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