Mining algorithm of spatial-temporal co-occurrence pattern based on vehicle GPS trajectory

Zhang Yongmei, Guo Sha, Xing Kuo, Liu Mengmeng
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引用次数: 2

Abstract

In the calculation process of spatial-temporal co-occurrence patterns, traditional methods often set the whole time frame as the actual existence time by default for all moving targets. However, in practice, existence time frame of different types is not necessarily the whole time frame. Based on this fact, the paper describes the calculation method of spatial-temporal interest degree-spatial frequency and time frequency in order to improve the practicability of co-occurrence patterns. In addition, the paper sets spatial-temporal weight coefficient for every pattern and sorts all candidates of co-occurrence patterns based on their weight. Then high efficiency co-occurrence patterns can be selected easily. Thus the proposed algorithm in this paper provides a solution to the difficulty of setting time thresholds and space thresholds in advance. And the experiment results show that the method can improve the effectiveness of spatial-temporal co-occurrence patterns simultaneously.
基于车载GPS轨迹的时空共现模式挖掘算法
在时空共现模式的计算过程中,传统方法通常默认将所有运动目标的实际存在时间设定为整个时间框架。然而,在实践中,不同类型的存在时间框架并不一定是整个时间框架。在此基础上,提出了时空兴趣度-空间频率和时间频率的计算方法,以提高共现模式的实用性。此外,本文还为每种模式设置了时空权重系数,并根据权重对所有候选共现模式进行了排序。这样就可以方便地选择高效共现模式。因此本文提出的算法解决了时间阈值和空间阈值难以提前设置的问题。实验结果表明,该方法可以同时提高时空共现模式的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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