论文标题
NLPMM：马尔可夫建模的下一个位置预测变量
NLPMM: a Next Location Predictor with Markov Modeling
论文作者
论文摘要
在本文中，我们解决了使用轨迹的历史数据集预测移动对象的下一个位置的问题。我们提出了Markov Modeling（NLPMM）的下一个位置预测器，该预测有以下优点：（1）它在进行预测时考虑了个体和集体运动模式，（2）即使轨迹数据稀疏，它也有效，（3）它考虑了时间因素并构建了适合不同时间周期的模型。我们已经在真实数据集中进行了广泛的实验，结果证明了NLPMM优于现有方法。
In this paper, we solve the problem of predicting the next locations of the moving objects with a historical dataset of trajectories. We present a Next Location Predictor with Markov Modeling (NLPMM) which has the following advantages: (1) it considers both individual and collective movement patterns in making prediction, (2) it is effective even when the trajectory data is sparse, (3) it considers the time factor and builds models that are suited to different time periods. We have conducted extensive experiments in a real dataset, and the results demonstrate the superiority of NLPMM over existing methods.
