论文标题
MPE：用于预测下一位置的移动性模式嵌入模型
MPE: A Mobility Pattern Embedding Model for Predicting Next Locations
论文作者
论文摘要
定位和照相设备的广泛使用量会增加一系列流量轨迹数据（例如，车辆通道记录和出租车轨迹数据），每个记录至少具有三个属性：对象ID，位置ID和时间戳记。在本文中，我们提出了一种新型的移动性模式嵌入称为MPE的模型，以阐明来自多个方面的流量轨迹数据中人们的活动性模式，包括顺序，个人和时间因素。 MPE具有两个显着特征：（1）它能够将各种类型的信息（对象，位置和时间）投放到集成的低维潜在空间； （2）它考虑了交通轨迹数据中的道路网络引起的``幻象过渡''的影响。这种嵌入模型为诸如下一个位置预测和可视化之类的广泛应用打开了大门。两个现实世界数据集的实验结果表明，MPE有效，并且在各种任务中的最先进方法都大大优于最先进的方法。
The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people's mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of ``phantom transitions'' arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.
