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Author(s): |
Junwei Wu, Shenyang Institute of Automation, Chinese Academy of SciencesShenyang 110016, China;University of Chinese Academy of Sciences Beijing 100039, China
Yunlong Zhu, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang 110016, China
Tao Ku, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang 110016, China
Liang Wang, Shenyang Institute of Automation, Chinese Academy of Sciences Shenyang 110016, China;University of Chinese Academy of Sciences Beijing 100039, China |
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Abstract: |
Positioning applications have recently become extremely popular thanks to recent advances in telecommunications and geopositioning reporting devices (GPS, PDA etc.). It is thus to be expected that all these devices will start to generate an unprecedented stream of time-stamped positions leading to storage and computation challenges. Hence the need for trajectory compression arises. Previously most work has been done in compression mainly deal with two-dimensional spatial data without taking into account another important dimension: time. Besides, very few of the existing spatiotemporal compression algrithms, which cannot determine whether the final compression result is optimal, use optimization algorithms such as Genetic Algorithm (GA) and Paticle Swam Optimation (PSO) algorithm. With these two points in mind, we propose a new trajectory compression algrithm based on the GA, PSO, and Douglas-Peucker algrithms to compress time-stamped position data. Experimental results illustrate that this compression method can achive large data compression ratio and high accuracy, and especially, can obtain the optimal or suboptimum compression result.
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