基于GPS数据的频繁轨迹挖掘

Saiph Savage, Shoji Nishimura, Norma Elva Chávez-Rodríguez, Xifeng Yan
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引用次数: 16

摘要

在本文中,我们提出了一种新的算法来寻找用户在日常生活中经常出现的路线,在我们的方法中,我们建立了一个网格,在这个网格中我们映射属于某个序列的每个GPS数据点。(我们认为每个序列都遵循一条路线)然后我们执行一个具有概率基础的插值过程,并找到用户轨迹的更精确描述。对于每条轨迹,我们找到了被交叉的边缘,我们用交叉的边缘创建了一个直方图,在这个直方图中,箱子表示交叉的边缘,频率值表示某个用户的边缘被交叉的次数。然后,我们选择K条最频繁的边,并将它们组合起来,以创建用户拥有的最频繁路径列表。我们将我们的结果与语义位置和路线的自适应学习[6]中提出的算法进行比较,发现我们与[6]相反的实现可以区分方向,即从a到B的路线和从B到a的路线被视为不同的路线。此外,我们的实施还允许对路线的分段进行分析,据我们所知,这在以前的相关工作中没有进行过。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequent trajectory mining on GPS data
In this paper we propose a new algorithm for finding the frequent routes that a user has in his daily routine, in our method we build a grid in which we map each of the GPS data points that belong to a certain sequence. (We consider that each sequence conforms a route) we then carry out an interpolation procedure that has a probabilistic basis and find a more precise description of the user's trajectory. For each trajectory we find the edges that were crossed, with the crossed edges we create a histogram in which the bins denote the crossed edges and the frequency value the number of times that edge was crossed for a certain user. We then select the K most frequent edges and combine them to create a list of the most frequent paths that a user has. We compared our results with the algorithm that was proposed in Adaptive learning of semantic locations and routes [6] to find frequent routes of a user, and found that our implementation on the contrary of [6] can discriminate directions, ie routes that go from A to B and routes that go from B to A are taken as different. Furthermore our implementation also permits the analysis of subsections of the routes, something that to our knowledge had not been carried out in previous related work.
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