Evaluation for typical compression method of trajectory data

doi:10.13474/j.cnki.11-2246.2019.0113

Abstract

Abstract:

One of the key bottlenecks in the big data of trajectories is the massive data size of the trajectory data. Therefore, the compression of trajectory data is the important field of the trajectory big data research. Existing trajectory compression algorithms emphasize the maintenance of the single dimensional space-time feature of the trajectory data, but lack the study of the impact of compression algorithm on the multi-dimensional space-time feature. In this paper, multi-dimensional space-time characteristics of trajectory data such as area error, distance error, direction error, speed error, compression rate and compression speed of MBR are selected for evaluation, and typical trajectory compression methods are evaluated from three levels of geometric features, motion features and compression efficiency of the trajectory. At the same time, in order to systematically observe the change of trajectory time and space characteristics of trajectory compression algorithm on different compression scales, this paper adopts the evaluation method of multiple scale compression results.Comprehensive research results show that the overall effect on considering the trajectory feature compression algorithms such as TD_TR algorithm to track the overall characteristics of time and space to keep the good, and the effect of different compression algorithms on the space-time characteristics of overall consistency with scale change.

Key words: trajectory data, trajectory compression, spatial-temporal characteristics, error evaluation

CLC Number:

P208

LIANG Ming, CHEN Wenjing, DUAN Ping, LI Jia. Evaluation for typical compression method of trajectory data[J]. 测绘通报, 2019, 0(4): 60-64,70.

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