用隐半马尔可夫模型重构个体活动轨迹

Zixuan Han, Zijian Wan, Wanyi Guo, Chang Ren
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引用次数: 0

摘要

通过众包收集到的人类活动的个体轨迹暗示了个体活动的特征规律。个体活动模式分析往往存在数据缺口,并引入了个体活动模式分析的负面影响。通过提取已知数据的内部规律,可以对位置和时间之间的不完全关系进行估算和重构。提出了一种基于个体行为模式和隐半马尔可夫模型(HSMM)的轨迹重建方法。它引入了行为状态变量和位置的离散表示,以方便解释个体日常活动中模型参数的实际意义。在HSMM中,各参数之间的相互作用模拟了行为状态的转变和时空变化。单个位置和行为状态之间的关系被捕获为单个活动模式。该模式用于推导所研究时间段内的个体状态变化序列。可以对状态序列进行解析,得到最终的单个活动轨迹。在包括微博签到和手机GPS记录在内的单个轨迹数据集上进行的实验表明,轨迹重建的准确率为84.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconstructing Individual Activity Trajectories by Hidden Semi-Markov Model
The individual trajectory of human activity collected through crowdsourcing implies the characteristic regularity of individual activity. There are always data gaps, and it introduces negative influence of individual activity pattern analyses. By extracting the internal regularity of known data, the incomplete relationship between location and time can be imputed and reconstructed. In this paper, a method of trajectory reconstruction based on individual behavior pattern and Hidden Semi-Markov Model (HSMM) is presented. It introduces behavior state variables and a discrete representation of locations to facilitate explanation of real-world meaning of the model parameters in individual daily activities. In HSMM, the interaction between various parameters imitates the behavior state transition and spatiotemporal variation. The relationship between the individual locations and behavior states is captured as an individual activity pattern. This pattern is used to derive individual state change sequence in the studied period of time. The state sequence can be resolved to obtain the final individual activity trajectory. Experiment on an individual track data set including Weibo check-ins and mobile phone GPS records showed that the accuracy of the trajectory reconstruction was 84.2%.
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