论文标题
TRALFM：流量轨迹数据的潜在因子建模
TraLFM: Latent Factor Modeling of Traffic Trajectory Data
论文作者
论文摘要
定位设备（例如GPS）的广泛使用已引起大量人类运动数据，通常是以轨迹的形式产生的。了解人类流动性模式可以使许多基于位置的应用程序受益。在本文中，我们通过潜在因子建模提出了一种称为TRALFM的新型生成模型，以挖掘交通轨迹的人类移动性模式。 TRALFM基于三个关键观察：（1）轨迹中的位置序列反映了人类的迁移率模式； （2）人类流动模式随着人而异； （3）人类流动模式往往是周期性的，并且随着时间的流逝而变化。因此，TRALFM以统一的方式对顺序，个人和时间因素的联合作用进行了建模，并为许多应用程序（例如潜在因素分析和下一个位置预测）带来了新的观点。我们对两个实际数据集进行了彻底的经验研究，实验结果证实，在这些应用中，TRALFM的表现明显优于最新方法。
The widespread use of positioning devices (e.g., GPS) has given rise to a vast body of human movement data, often in the form of trajectories. Understanding human mobility patterns could benefit many location-based applications. In this paper, we propose a novel generative model called TraLFM via latent factor modeling to mine human mobility patterns underlying traffic trajectories. TraLFM is based on three key observations: (1) human mobility patterns are reflected by the sequences of locations in the trajectories; (2) human mobility patterns vary with people; and (3) human mobility patterns tend to be cyclical and change over time. Thus, TraLFM models the joint action of sequential, personal and temporal factors in a unified way, and brings a new perspective to many applications such as latent factor analysis and next location prediction. We perform thorough empirical studies on two real datasets, and the experimental results confirm that TraLFM outperforms the state-of-the-art methods significantly in these applications.
