Inferring human mobility using communication patterns

2008

1 ABSTRACT This paper presents a new technique for monitoring human mobility in a territorial system based on cellphone activity. Considering that cells are the areas covered by the signal transmitted by an antenna that records the number of users connected while they make a call, mobile-telephone traffic data was monitored for each cell in a wide region. Based on this data regarding the position and intensity of mobile phone use, it is possible to monitor daily variations and how these variations change over time. Based on the statistics, we are in a position to presume that the telephone traffic in a certain region is indicative of the effective population density and its variation in 24 hours. While exploiting new techniques developed for mobile phone systems, this study presents a spatial analysis of social dynamics in urban areas. Mobile phone activity data was represented in a series of maps in order to visualize patterns of geo-demography in a given territory and to provide a...

Self-Organized Mobile Communication Technologies and Techniques for Network Optimization

The 5th Generation (5G) of mobile communication networks is being developed to address the demands and business contexts of 2020 and beyond. Its vision is to enable a fully mobile and connected society and also to trigger socio-economic transformations in ways eventually unimagined today. This means that the physical world to be covered with planned 5G networks including communication networks, humans and objects is becoming a type of an information system. So as to improve the experience of individuals, communities, societies, etc. within such systems, a thorough comprehension of intelligence processes responsible of generating, handling and controlling those data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have crucial role in forthcoming communication technologies. Human mobility patterns models can be categorized into synthetic, trace-based and community-ba...

Journal of Urban Technology, 2014

In the last decade, mobile phones and mobile devices using mobile cellular telecommunication network connections have become ubiquitous. In several developed countries, the penetration of such devices has surpassed 100 percent. They facilitate communication and access to large quantities of data without the requirement of a fixed location or connection. Assuming mobile phones usually are in close proximity with the user, their cellular activities and locations are indicative of the user's activities and movements. As such, those cellular devices may be considered as a large scale distributed human activity sensing platform. This paper uses mobile operator telephony data to visualize the regional flows of people across the Republic of Ireland. In addition, the use of modified Markov chains for the ranking of significant regions of interest to mobile subscribers is investigated. Methodology is then presented which demonstrates how the ranking of significant regions of interest may be used to estimate national population, results of which are found to have strong correlation with census data.

Scientific Reports, 2013

In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy. S tudies of mobility patterns and predictions of individual mobility trajectories are important in many research fields, such as mobile computing, epidemic modeling, traffic planning and disaster response 1-3 . Real-time locations visited by individuals are typically collected through mobile devices equipped with global-positioning system (GPS) capability, mobile phone cell towers, or wireless local area network (WLAN) access points.

This paper investigates the Data for Development (D4D) challenge [3], an open challenge set by the French mobile phone company, Orange, who have provided anonymized records of their customers in the Ivory Coast. This data spans a 5 month (150 day) horizon spread across 4 different sets containing antenna-to-antenna traffic, trace data for 50,000 customers at varying spatial resolution, and social graphs for 5,000 customers. By leveraging cloud-based and open-source analytics infrastructure to (1) merge the D4D datasets with Geographic Information System (GIS) data and (2) apply data mining algorithms, this paper presents a number of techniques for detecting mobility patterns of Orange customers in the Ivory Coast. By applying a k-medoid clustering algorithm to the antenna locations and their average distance to nearby antennas , we show how the high spatial resolution mobile phone dataset reveals a number of daily mobility patterns and properties , including trends in weekday versus weekend, public holiday mobility behavior, and distributional properties of daily trip distances across each cluster. With a view towards providing tools to assist with transport infrastructure planning , we combine the high spatial resolution D4D dataset with GIS data for transport infrastructure and demonstrate an approach for detecting whether a mobile phone user is traveling on a segment of transport infrastructure. This work culminates in a preliminary cloud-based GIS tool for visualizing mobility traces.

PLOS ONE, 2015

In the past decade, large scale mobile phone data have become available for the study of human movement patterns. These data hold an immense promise for understanding human behavior on a vast scale, and with a precision and accuracy never before possible with censuses, surveys or other existing data collection techniques. There is already a significant body of literature that has made key inroads into understanding human mobility using this exciting new data source, and there have been several different measures of mobility used. However, existing mobile phone based mobility measures are inconsistent, inaccurate, and confounded with social characteristics of local context. New measures would best be developed immediately as they will influence future studies of mobility using mobile phone data. In this article, we do exactly this. We discuss problems with existing mobile phone based measures of mobility and describe new methods for measuring mobility that address these concerns. Our measures of mobility, which incorporate both mobile phone records and detailed GIS data, are designed to address the spatial nature of human mobility, to remain independent of social characteristics of context, and to be comparable across geographic regions and time. We also contribute a discussion of the variety of uses for these new measures in developing a better understanding of how human mobility influences micro-level human behaviors and well-being, and macro-level social organization and change.

Physica A: Statistical Mechanics and its Applications, 2013

Mobile phone datasets allow for the analysis of human behavior on an unprecedented scale. The social network, temporal dynamics and mobile behavior of mobile phone users have often been analyzed independently from each other using mobile phone datasets. In this article, we explore the connections between various features of human behavior extracted from a large mobile phone dataset. Our observations are based on the analysis of communication data of 100000 anonymized and randomly chosen individuals in a dataset of communications in Portugal. We show that clustering and principal component analysis allow for a significant dimension reduction with limited loss of information. The most important features are related to geographical location. In particular, we observe that most people spend most of their time at only a few locations. With the help of clustering methods, we then robustly identify home and office locations and compare the results with official census data. Finally, we analyze the geographic spread of users' frequent locations and show that commuting distances can be reasonably well explained by a gravity model.

2020

Modelling human mobility is crucial in several areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. On the other hand, those models that capture some social aspects of human mobility have trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose STS-EPR, a modeling framework that embeds mechanisms to capture the spatial, temporal, and social aspects together. Our experiments show that STS-EPR outperforms existing spatial-temporal or social models on a set of standard mobility metrics and that it can be used with a limited amount of information without any significant loss of realism. STS-EPR, which is open-source and tested on open data, is a step towards the design of mechanistic models that can capture all th...

PLoS ONE, 2014

The pervasive use of new mobile devices has allowed a better characterization in space and time of human concentrations and mobility in general. Besides its theoretical interest, describing mobility is of great importance for a number of practical applications ranging from the forecast of disease spreading to the design of new spaces in urban environments. While classical data sources, such as surveys or census, have a limited level of geographical resolution (e.g., districts, municipalities, counties are typically used) or are restricted to generic workdays or weekends, the data coming from mobile devices can be precisely located both in time and space. Most previous works have used a single data source to study human mobility patterns. Here we perform instead a cross-check analysis by comparing results obtained with data collected from three different sources: Twitter, census and cell phones. The analysis is focused on the urban areas of Barcelona and Madrid, for which data of the three types is available. We assess the correlation between the datasets on different aspects: the spatial distribution of people concentration, the temporal evolution of people density and the mobility patterns of individuals. Our results show that the three data sources are providing comparable information. Even though the representativeness of Twitter geolocated data is lower than that of mobile phone and census data, the correlations between the population density profiles and mobility patterns detected by the three datasets are close to one in a grid with cells of 2 × 2 and 1 × 1 square kilometers. This level of correlation supports the feasibility of interchanging the three data sources at the spatio-temporal scales considered.

The Big Data, originating from the digital breadcrumbs of human activities, sensed as a by-product of the technologies that we use for our daily activities, let us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data "proxies", as the mobile calls data for mobility. In this paper we investigate to what extent such "big data", in integration with administrative ones, could be a support in producing reliable and timely estimates of inter-city mobility. The study has been jointly developed by Istat, CNR, University of Pisa in the range of interest of the "Commssione di studio avente il compito di orientare le scelte dellIstat sul tema dei Big Data ". In an ongoing project at ISTAT, called "Persons and Places"-based on an integration of administrative data sources, it has been produced a first release of Origin Destination matrix-at municipality level-assuming that the places of residence and that of work (or study) be the terminal points of usual individual mobility for work or study. The coincidence between the city of residence and that of work (or study)is considered as a proxy of the absence of intercity mobility for a person (we define him a static resident). The opposite case is considered as a proxy of presence of mobility (the person is a dynamic resident: commuter or embedded). As administrative data do not contain information on frequency of the mobility, the idea is to specify an estimate method, using calling data as support, to define for each municipality the stock of standing residents, embedded city users and daily city users (commuters).