Trajectory Similarity Measure Based on Multiple Movement Features

ZHU Jin; HU Bin; SHAO Hua

doi:10.13203/j.whugis20150594

Volume 42 Issue 12

Dec. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(12): 1703-1710. > DOI: 10.13203/j.whugis20150594

ZHU Jin, HU Bin, SHAO Hua. Trajectory Similarity Measure Based on Multiple Movement Features[J]. Geomatics and Information Science of Wuhan University, 2017, 42(12): 1703-1710. DOI: 10.13203/j.whugis20150594

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Trajectory Similarity Measure Based on Multiple Movement Features

ZHU Jin^1,,
HU Bin^{2, 3, ,},
SHAO Hua⁴

1.
School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
2.
Key Laboratory for Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
3.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4.
Department of Geomatics Engineering, Nanjing University of Technology, Nanjing 210009, China

Funds:

The National Natural Science Foundation of China 41571389

The National Natural Science Foundation of China 41501431

Teacher Training Research Funding Project of Suzhou University of Science and Technology 331511203

Youth Foundation Project of Suzhou University of Science and Technology 341731204

More Information

Author Bio:
ZHU Jin, PhD, lecturer, specializes in trajectory data mining. E-mail: 540896749@qq.com
Corresponding author:
HU Bin, PhD, associate professor. E-mail: hb_hubin@126.com
Received Date: April 10, 2016
Published Date: December 04, 2017

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Abstract

Abstract

For the shortcoming that existing methods can only measure the trajectory similarity of single movement feature (e.g. velocity, acceleration), the trajectory similarity measure based on multiple movement features is proposed. The measure is significant for analyzing and understanding the movement behaviors and mechanisms of moving objects. The measure borrows the idea of data cube, quantizes and symbolizes the multiple movement feature time series. In multiple movement feature domain space, the Euclidean distances between characters are computed as the substitution costs of weighted edit distance which is computed as the similarity measure. The measure is integrated with the spectral clustering method for movement sequential pattern discovery. Using the hurricane dataset, the known hurricane originating and movement laws in meteorological literatures verify the effectiveness of the measure.
- movement features,
- trajectory similarity measure,
- weighted edit distance,
- spectral clustering,
- movement sequential pattern

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