Next place prediction by understanding mobility patterns
Next place prediction by understanding mobility patterns
复制标题
通过了解流动模式预测下一个地点
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
A. Nash
中科院分区：
文献类型：
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作者：
M. Dash;Kee Kiat Koo;J. Gomes;S. Krishnaswamy;Daniel Rugeles;A. Nash
随着连接世界各地人们的技术不断进步，在利用这种连接产生的数据方面也应该有相应的进步。为此，下一个位置预测是移动数据的重要问题。在本文中，我们提出了几个模型，使用动态贝叶斯网络（DBN）。这些模型的开发背后的想法来自用户的典型日常移动模式。三个特征（位置、星期几（DoW）和时间（ToD））及其组合用于开发这些模型。知道不是所有的模型都适用于所有情况，我们开发了三个组合模型，使用最小熵，最高概率和合奏。进行了广泛的性能研究，比较这些模型在两个不同的移动数据集：CDR数据和诺基亚移动的数据，这是基于GPS。结果表明，最小熵和最高概率DBN表现最好。
As technology to connect people across the world is advancing, there should be corresponding advancement in taking advantage of data that is generated out of such connection. To that end, next place prediction is an important problem for mobility data. In this paper we propose several models using dynamic Bayesian network (DBN). Idea behind development of these models come from typical daily mobility patterns a user have. Three features (location, day of the week (DoW), and time of the day (ToD)) and their combinations are used to develop these models. Knowing that not all models work well for all situations, we developed three combined models using least entropy, highest probability and ensemble. Extensive performance study is conducted to compare these models over two different mobility data sets: a CDR data and Nokia mobile data which is based on GPS. Results show that least entropy and highest probability DBNs perform the best.