Qi Mengjun, Wang Zhongyuan, He Zheng, Shao Zhenfeng
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072, China.
Shenzhen Research Institute of Wuhan University, Shenzhen 518057, China.
Sensors (Basel). 2019 May 7;19(9):2102. doi: 10.3390/s19092102.
With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification.
随着基于位置的服务和应用的普及，产生了大量的移动性数据。通过移动轨迹信息进行身份识别，尤其是异步轨迹数据，在社会治安防控方面引起了极大关注。本文提出了一种基于用户轨迹中分布最频繁的TOP-N（关于用户轨迹的分布最频繁的N个区域）区域的身份识别解决方法。该方法首先找到轨迹点分布最频繁的TOP-N区域，以降低计算复杂度。在此基础上，我们讨论了三种轨迹相似性度量方法，用于匹配两个数据集中属于同一用户的轨迹。我们在两个真实的GPS轨迹数据集GeoLife和Cabspotting上进行了广泛的实验，并全面讨论了实验结果。实验表明，我们的方法在用户身份识别方面具有显著的有效性和效率。
