Abstract In this study, with Estonia as an example,we established an approach based on Hidden Markov Model to extract large-scale commuting patterns at different geographical levels using a massive amount of mobile phone cellular network data, which is referred to as Call detail record (CDR). The proposed model is designed for reconstructing and transforming the trajectories extracted from the CDR data. This step allowed us to perform origin-destination matrix extraction among different geographical levels, which helped in depicting the commuting patterns. Besides, we introduced different techniques for analyzing the commuting at the urban level. Our results unveiled that there is great potential behind mobile data of the cellular networks after transforming it into meaningful mobility patterns. That can easily be used for understanding urban dynamics, large-scale daily commuting and mobility. The aggressive development and growth of ubiquitous mobile sensing have generated valuable data that can be used with our approach for providing answers and solutions to the growing problems of transportation, urbanization and sustainability.

中文翻译：

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揭示基于手机蜂窝网络数据的大规模通勤模式

摘要 在本研究中，以爱沙尼亚为例，我们建立了一种基于隐马尔科夫模型的方法，利用海量的手机蜂窝网络数据，提取不同地理层次的大规模通勤模式，称为呼叫详细记录。 (CDR)。所提出的模型旨在重建和转换从 CDR 数据中提取的轨迹。这一步使我们能够在不同的地理级别之间执行起点-终点矩阵提取，这有助于描绘通勤模式。此外，我们介绍了不同的技术来分析城市层面的通勤。我们的研究结果表明，在将蜂窝网络的移动数据转化为有意义的移动模式后，它具有巨大的潜力。这可以很容易地用于了解城市动态、大规模的日常通勤和流动性。无处不在的移动传感的积极发展和增长产生了宝贵的数据，这些数据可用于我们的方法，为日益严重的交通、城市化和可持续性问题提供答案和解决方案。