Unveiling the complexity of human mobility by querying and mining massive trajectory data

The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people’s travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further analyses and mining, and the incremental mining strategies to address scalability.     

Understanding Human Mobility and Workload Dynamics Due to Different Large-Scale Events Using Mobile Phone Data

The analysis of mobile phone data can help carriers to improve the way they deal with unusual workloads imposed by large-scale events. This paper analyzes human mobility and the resulting dynamics in the network workload caused by three different types of large-scale events: a major soccer match, a rock concert, and a New Year’s Eve celebration, which took place in a large Brazilian city. Our analysis is based on the characterization of records of mobile phone calls made around the time and place of each event. That is, human mobility and network workload are analyzed in terms of the number of mobile phone calls, their inter-arrival and inter-departure times, and their durations. We use heat maps to visually analyze the spatio-temporal dynamics of the movement patterns of the participants of the large-scale event. The results obtained can be helpful to improve the understanding of human mobility caused by large-scale events. Such results could also provide valuable insights for network managers into effective capacity management and planning strategies. We also present PrediTraf, an application built to help the cellphone carriers plan their infrastructure on large-scale events.     

Adapting the mobile phone for task efficiency: the case of predicting outgoing calls using frequency and regularity of historical calls

The set of functionalities provided by advanced mobile phones is significantly increasing. However, the small size of mobile phone user interfaces makes it difficult for the user to deal with this large number of functionalities, which could reflect negatively on user performance and the efficiency of mobile phone functionalities. In this paper, we designed and developed an adaptive task-based functionality called ATF on mobile phones, where the task we focused on was to predict the next contact that the user is most likely to call. Furthermore, we conducted comprehensive evaluation of our approach. We show that our approach can successfully predict contacts that a user will most likely call next. Our results uncover the frequency and pattern of regularity in making mobile phone calls and suggest promising avenues for future work for optimising tasks (beyond phone calls) performed with the mobile phone.     

Correlating mobile phone usage and travel behavior – A case study of Harbin,China

Information and communication technologies (ICTs), such as mobile phones and the Internet, are increasingly pervasive in modern society. These technologies provide new resources for spatio-temporal data mining and geographic knowledge discovery. Since the development of ICTs also impacts physical movement of individuals in societies, much of the existing research has focused on examining the correlation between ICT and human mobility. In this paper, we aim to provide a deeper understanding of how usage of mobile phones correlates with individual travel behavior by exploring the correlation between mobile phone call frequencies and three indicators of travel behavior: (1) radius, (2) eccentricity, and (3) entropy. The methodology is applied to a large dataset from Harbin city in China. The statistical analysis indicates a significant correlation between mobile phone usage and all of the three indicators. In addition, we examine and demonstrate how explanatory factors, such as age, gender, social temporal orders and characteristics of the built environment, impact the relationship between mobile phone usage and individual activity behavior.     

A regression-based approach for mining user movement patterns from random sample data

Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users.     

基于云安全的骚扰电话过滤系统研究

智能手机用户对通信安全的需求日益增加,本文研究目前骚扰电话过滤系统现状,提出一种基于云安全技术的设计方案,对其中的技术做了深入的研究。该系统将过滤服务器集群和大量手机整合到一个云安全体系中,实现对骚扰电话的快速和有效过滤。     

Context Awareness and Mobile Phones

This paper investigates some aspects of how context-awareness can support users of mobile phones, in particular the calling party. The use of mobile and stationary phones is discussed in relation to situational properties of a phone conversation, especially with regards to who might benefit from context-awareness in this context. An initial hypothesis is that mobile phone users communicate context information to each other (verbally) to a much higher degree than do stationary phone users. Mobile phone users could benefit much from context awareness technology, in particular when about to make a call, if they can receive context information regarding the person they are trying to reach prior to establishing the call. We argue that such technology should require low amounts of explicit user interaction, and could lead to less disrupting calls in inappropriate moments, as well as less frustration for the calling party when a call is not answered.     

Detecting criminal organizations in mobile phone networks

The study of criminal networks using traces from heterogeneous communication media is acquiring increasing importance in nowadays society. The usage of communication media such as mobile phones and online social networks leaves digital traces in the form of metadata that can be used for this type of analysis. The goal of this work is twofold: first we provide a theoretical framework for the problem of detecting and characterizing criminal organizations in networks reconstructed from phone call records. Then, we introduce an expert system to support law enforcement agencies in the task of unveiling the underlying structure of criminal networks hidden in communication data. This platform allows for statistical network analysis, community detection and visual exploration of mobile phone network data. It enables forensic investigators to deeply understand hierarchies within criminal organizations, discovering members who play central role and provide connection among sub-groups. Our work concludes illustrating the adoption of our computational framework for a real-word criminal investigation.     

基于Android平台的盲人手机系统设计与开发

人们生活中的绝大部分信息都是通过视觉获得的,所以盲人能够从外界获取的信息量很少,但这并不能阻止他们努力提高生活质量的渴望。智能手机的快速发展给盲人提供了前所未有的机遇。开发盲人手机具有极大的应用价值。该文主要介绍了在Android平台上设计和实现的一款盲人手机系统,该系统使用了语音识别、语音合成及Web Service等相关技术,实现了语音拨打电话、语音接听电话、语音发送短信、语音播报来电短信、语音报时、语音播报日期和语音播报天气等功能,并能在Android手机中稳定运行,方便盲人使用手机,具有较高的实用价值。     

Mapping relationships between mobile phone call activity and regional function using self-organizing map

Mobile phone data help us to understand human activities. Researchers have investigated the characteristics and relationships of human activities and regional function using information from physical and virtual spaces. However, how to establish location mapping between spaces to explore the relationships between mobile phone call activity and regional function remains unclear. In this paper, we employ a self-organizing map (SOM) to map locations with 24-dimensional activity attributes and identify relationships between users' mobile phone call activities and regional functions. We apply mobile phone call data from Harbin, a city in northeast China, to build the location mapping relationships between user clusters of mobile phone call activity and points of interest (POI) composition in geographical space. The results indicate that for mobile phone call activities, mobile phone users are mapped to five locations that represent particular mobile phone call patterns. Regarding regional functions, we identified nine unique types of functional areas that are related to production, business, entertainment and education according to the patterns of users and POI proportions. We then explored the correlations between users and POIs for each type of area. The results of this research provide new insights into the relationships between human activity and regional functions.