Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data
Understanding Individual Mobility Pattern and Portrait Depiction Based on Mobile Phone Data
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DOI：
10.3390/ijgi9110666
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发表时间：
2020-11-01
影响因子：
3.4
通讯作者：
Wu, Zheng
中科院分区：
文献类型：
--
作者：
Li, Chengming;Hu, Jiaxi;Wu, Zheng
随着大数据时代的到来，手机数据因其信息丰富、采样率高而受到越来越多的关注。目前，研究人员利用手机数据进行了各种研究。然而，现有研究大多集中于宏观分析，如城市热点检测、短期人群行为分析等。随着智慧城市的发展，个人服务和管理变得非常重要，因此基于大数据的个人微观肖像研究和移动模式十分必要。因此，本文首先提出一种描绘个体流动模式的方法，并基于微软亚洲研究院Geolife项目中北京志愿者的长期手机数据（2007年至2012年），进行更详细的个体肖像描绘分析。结论如下：（1）基于高密度聚类识别，将志愿者的行为轨迹概括为三种类型，其中，两点一线轨迹和均匀分布的行为轨迹在北京较为普遍。 (2)结合Google Maps数据，详细分析了5名志愿者的行为轨迹和个体的活动模式，提出了一种综合考虑职业、爱好等属性的个体特征肖像刻画方法。 （3）通过对部分志愿者的个体特征分析发现，两点一线的个体普遍为企事业单位白领，单一集群的情况主要存在于大学生和家庭自由职业者中。本研究结果对于大数据时代的个体分类和预测具有重要意义，也可为智慧城市的精准服务和个性化管理提供有益的指导。
With the arrival of the big data era, mobile phone data have attracted increasing attention due to their rich information and high sampling rate. Currently, researchers have conducted various studies using mobile phone data. However, most existing studies have focused on macroscopic analysis, such as urban hot spot detection and crowd behavior analysis over a short period. With the development of the smart city, personal service and management have become very important, so microscopic portraiture research and mobility pattern of an individual based on big data is necessary. Therefore, this paper first proposes a method to depict the individual mobility pattern, and based on the long-term mobile phone data (from 2007 to 2012) of volunteers from Beijing as part of project Geolife conducted by Microsoft Research Asia, more detailed individual portrait depiction analysis is performed. The conclusions are as follows: (1) Based on high-density cluster identification, the behavior trajectories of volunteers are generalized into three types, and among them, the two-point-one-line trajectory and evenly distributed behavior trajectory were more prevalent in Beijing. (2) By integrating with Google Maps data, five volunteers' behavior trajectories and the activity patterns of individuals were analyzed in detail, and a portrait depiction method for individual characteristics comprehensively considering their attributes, such as occupation and hobbies, is proposed. (3) Based on analysis of the individual characteristics of some volunteers, it is discovered that two-point-one-line individuals are generally white-collar workers working in enterprises or institutions, and the situation of a single cluster mainly exists among college students and home freelancer. The findings of this study are important for individual classification and prediction in the big data era and can also provide useful guidance for targeted services and individualized management of smart cities.