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Spatiotemporal data, and more specifically origin-destination matrices, are critical inputs to mobility studies for transportation planning and urban management purposes. Traditionally, high-cost and hard-to-update household travel surveys are used to produce large-scale origin-destination flow information of individuals' whereabouts. In this paper, we propose a methodology to estimate origin-destination (O-D) matrices based on passively-collected cellular network signalling data of millions of anonymous mobile phone users in the Rhone-Alpes region, France. Unlike Call Detail Record (CDR) data which rely only on phone usage, signalling data include all network-based records providing higher spatiotemporal granularity. The explored dataset, which consists of time-stamped traces from 2G and 3G cellular networks with users' unique identifier and cell tower locations, is used to first analyse the cell phone activity degree indicators of each user in order to qualify the mobility information involved in these records. These indicators serve as filtering criteria to identify users whose device transactions are sufficiently distributed over the analysed period to allow studying their mobility. Trips are then extracted from the spatiotemporal traces of users for whom the home location could be detected. Trips have been derived based on a minimum stationary time assumption that enables to determine activity (stop) zones for each user. As a large, but still partial, fraction of the population is observed, scaling is required to obtain an O-D matrix for the full population. We propose a method to perform this scaling and we show that signalling data-based O-D matrix carries similar estimations as those that can be obtained via travel surveys.

Reliable and timely information on socio-economic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social disparity, but their lack of interpretability, accuracy or scale has limited their relevance to date. We investigate the divide in digital mobile service usage with a large dataset of 3.7 billion time-stamped and geo-referenced mobile traffic records in a major European country, and find profound geographical unevenness in mobile service usage -especially on news, e-mail, social media consumption and audio/video streaming. We relate such diversity with income, educational attainment and inequality, and reveal how low-income or low-education areas are more likely to engage in video streaming or social media and less in news consumption, information searching, e-mail or audio streaming. The digital usage gap is so large that we can accurately infer the socio-economic status of a small area or even its Gini coefficient only from aggregated data traffic. Our results make the case for an inexpensive, privacy-preserving, real-time and scalable way to understand the digital usage divide and, in turn, poverty, unemployment or economic growth in our societies through mobile phone data. This work has been supported by the research project CANCAN (Content and Context based Adaptation in Mobile Networks), grant no. ANR-18-CE25-0011, funded by the French National Research Agency (ANR). The work of M.F. was partially supported by the Atracción de Talento Investigador grant no. 2019-T1/TIC-16037 NetSense, funded by Comunidad de Madrid. E.M. and I.U. acknowledge partial support by Ministerio de Economía, Industria y Competitividad, Gobierno de España, grant nos. FIS2016-78904-C3-3-P and PID2019-106811GB-C32.

Reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people's lives - food, education, healthcare. It spurs new economic opportunities, thus improving livelihoods. Using a comprehensive dataset of pseudonymized mobile phone records, we analyse the impact of electrification on attractiveness for rural areas in Senegal. We extract communication and mobility flows from call detail records and show that electrification is positively and specifically correlated with centrality measures within the communication network and with the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination and the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than centralizing electricity supplies to maximize the development of specifically targeted sites., Royal Society Open Science, 8 (10), ISSN:2054-5703

Call detail records (CDR) collected by mobile phone network providers have been largely used to model and analyze human-centric mobility. Despite their potential, they are limited in terms of both spatial and temporal accuracy thus being unable to capture detailed human mobility information. Network Signaling Data (NSD) represent a much richer source of spatio-temporal information currently collected by network providers, but mostly unexploited for fine-grained reconstruction of human-centric trajectories. In this paper, we present TRANSIT, TRAjectory inference from Network SIgnaling daTa, a novel framework capable of processing NSD to accurately distinguish mobility phases from stationary activities for individual mobile devices, and reconstruct, at scale, fine-grained human mobility trajectories, by exploiting, with a DBSCAN-based clustering approach, the inherent recurrence of human mobility and the higher sampling rate of NSD. The validation on a ground-truth dataset of GPS trajectories showcases the superior performance of TRANSIT (80% precision and 96% recall) with respect to state-of-the-art solutions in the identification of movement periods, as well as an average 190 m spatial accuracy in the estimation of the trajectories. We also leverage TRANSIT to process a unique large-scale NSD dataset of more than 10 millions of individuals and perform an exploratory analysis of city-wide transport mode shares, recurrent commuting paths, urban attractivity and analysis of mobility flows. TRUE pub

Estimating migration flows and forecasting future trends is important, both to understand the causes and effects of migration and to implement policies directed at supplying particular services. Over the years, less research has been done on modeling migration flows than the efforts allocated to modeling other flow types, for instance, commute. Limited data availability has been one of the major impediments for empirical analyses and for theoretical advances in the modeling of migration flows. As a migration trip takes place much less frequent compared to the commute, it requires a longitudinal set of data for the analysis. This study makes use a massive mobile phone network data to infer migration trips and their distribution. Insightful characteristics of the inferred migration trips are revealed, such as intra/inter-district migration flows, migration distance distribution, and origin-destination (O-D) movements. For migration trip distribution modelling, log-linear model, traditional gravity model, and recently introduced radiation model were examined with different approaches taken in defining parameters for each model. As the result, the gravity and log-linear models with a direct distance (displacement) used as its travel cost and district centroids used as the reference points perform best among the other alternative models. A radiation model that considers district population performs best among the radiation models, but worse than that of the gravity and log-linear models.

In this work, we discuss how an existing algorithm to extract long-distance trips from mobile phone data (Janzen et al., 2016 a,b) can be supplemented with man-made heuristics to arrive at plausible domestic tourism trips. In total, we detect 18,380 domestic tourism trips from mobile phone data of 69,000 users sampled in 32 cities in France. By analysing temporal, spatial and social characteristics of the domestic tourism trips, we explore several possible directions for the use of mobile phone data when studying domestic tourism on a large scale. We show how temporal patterns of mobile phone use differ from baseline behaviour, how destinations of trips can algorithmically be derived, and how future research can integrate information on the activation of the social network. Our main contribution is that we describe current barriers and sketch direction for future research. Nous discutons ici comment un algorithme d’extraction des trajets longue distance à partir de données de téléphonie mobile (Janzen et al., 2016 a,b) peut être complété par des heuristiques spécifiques afin de détecter des déplacements touristiques. Au total, nous avons détecté 18 380 voyages de tourisme interne à partir de données de 69 000 utilisateurs du mobile échantillonnés dans 32 villes françaises. En analysant les caractéristiques temporelles, spatiales et sociales de ces voyages, nous montrons comment les usages du téléphone mobile lors de ces déplacements diffèrent du comportement ordinaire, comment les destinations de voyages peuvent être dérivées algorithmiquement et comment l’information sur l’activation du réseau social pourrait être intégrée à la recherche future. L’objectif principal de ce travail est d’esquisser quelques directions possibles de l’utilisation des données collectées automatiquement par les opérateurs de télécommunication dans l’étude du tourisme interne à grande échelle.

A longitudinal mobile phone data that include both location and communication logs is analyzed to infer social influence in terms of ego-network effect in the commute mode choice. The results show that person’s strong ties are more important to determine if driving is the person’s transport mode choice, whereas weak ties are more important to determine if public transit is the person’s choice. It is also evident from the results that social ties that are geographically closer are more influential for the commute mode choice than the ones who are farther away. For public transit, access distance is also one of the influential factors. The portion of transit users decreases as the access distance becomes larger. Moreover, social network is shown to influence the commute mode choice, as the likelihood of choosing a particular mode choice rises with the portion of social ties choosing that specific mode.

Large-scale location based traces, such as mobile phone data, have been identified as a promising data source to complement or even enrich official statistics. In many cases, a prerequisite step to deploy the massively gathered data is the detection of home location from individual users. The problem is that little research exists on the validation (comparison with ground truth datasets) or the uncertainty estimation of home detection methods, not at individual user level, nor at nation-wide levels. In this paper, we present an extensive empirical analysis of home detection methods when performed on a nation-wide mobile phone dataset from France. We analyze the validity of 9 different Home Detection Algorithms (HDAs), and we assess different sources of uncertainty. Based on 225 different set-ups for the home detection of around 18 million users we discuss different measures for validation and investigate sensitivity to user choices such as HDA parameter choice and observation period restriction. Our findings show that nation-wide performance of home detection is moderate at best, with correlations to ground truth maximizing at 0.60 only. Additionally, we show that time and duration of observation have a clear effect on performance, and that the effect of HDA criteria and parameter choice are rather small compared to other uncertainties. Our findings and discussion offer welcoming insights to other practitioners who want to apply home detection on similar datasets, or who are in need of an assessment of the challenges and uncertainties related to mobilizing mobile phone data for official statistics., Comment: 18 pages, 8 figures, 1 table. Winner of the student paper award of the BigSurv18 conference

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., Comment: 16 pages, 12 figures

International audience; Urban landscapes present a variety of socio-topological environments that are associated to diverse human activities. As the latter affect the way individuals connect with each other, a bound exists between the urban tissue and the mobile communication demand. In this paper, we investigate the heterogeneous patterns emerging in the mobile communication activity recorded within metropolitan regions. To that end, we introduce an original technique to identify classes of mobile traffic signatures that are distinctive of different urban fabrics. Our proposed technique outperforms previous approaches when confronted to ground-truth information, and allows characterizing the mobile demand in greater detail than that attained in the literature to date. We apply our technique to extensive real-world data collected by major mobile operators in ten cities. Results unveil the diversity of baseline communication activities across countries, but also evidence the existence of a number of mobile traffic signatures that are common to all studied areas and specific to particular land uses.

Extracting Ego-Centered Networks From Very Large Social Networks: This article presents a method at the intersection of macro and micro approaches in social networks. In examining ego-centered network, it proposes an approach to this type of study for very large networks. The calculation of several local indicators makes it possible to create aggregate statistics for each of the nodes of the network, which can then be seen as a corpus of ego-centered networks. An application is made on a network of telephone conversations between two million people, and a comparison is made with a classic ego-centered network study, based on an ethnographic research project. Social Networks, Ego-Centered Networks, Large Social Networks, Density, Communication Networks, Macro versus Micro, Qualitative Approaches (a corrected machine translation in English of this article is available on request from bms@reveus.org). Resume. Cet article presente une methode a l'intersection des approches « macro » et « micro » de l'analyse des reseaux sociaux. Prenant pour objectif l'etude de reseaux egocentres, il propose une demarche permettant ce type d'etude sur de tres grands reseaux. Le calcul de plusieurs indicateurs locaux peut en effet permettre de constituer des statistiques globales sur chacun des nœuds du reseau, qui sera alors vu comme un corpus de reseaux egocentres. Une mise en pratique est realisee sur un reseau d'echanges telephoniques entre deux millions d'individus et une comparaison est etablie avec une etude egocentree classique, s'appuyant sur une enquete ethnographique. Reseaux sociaux, Reseaux egocentres, Grands reseaux, Densite, Reseaux de communication, Macro vs. micro, Approches qualitatives. On oppose parfois deux axes dans l'analyse des reseaux 1 : l'un s'interessant a la structure d'un ensemble de liens, entre des individus possiblement tres nombreux

We report here on research aiming to reconstruct urban mobilities and communication practices through mobile phone base data. We have developed a software probe that can be implemented on a user's mobile phone, and which allows the joint recording and collection of the successive locations experienced by the user (through the identification of the cell in which the mobile phone is located) and all types of communicative acts performed through the mobile phone. This has been combined to indepth interviews with subjects over one week of their mobility and mobile communication behaviour. The method has been tested over a sample of 24 adults living in Paris, all in the 30–45 age range, half male and half female, with varying histories of mobility and professional flexibility constraints, in order to reconstruct their mobility and their communication-based activity spaces. We show how such a method enables the construction of a long time perspective on mobilities, and particularly on the articulation of displacements and mobile communication, which is an important issue in the ‘new mobilities paradigm’. We show how, over longer periods, mobility and communication practices combine into patterns marking social integration (or disintegration). We also show how our method allows us to construct new types of indicators, such as the propensity to communicate from a given type of place per unit of time, that reveal underlying patterns such as a higher propensity to call in mobile situations and transitory locations. This type of approach may be particularly relevant to the ongoing convergence of transport and communication studies, and to bridge the gap between communication research and mobility studies.

The common characteristic of Information and Communication Technology is that of being highly portable. Nevertheless, the dependence on the telecommunication operator's network and current communications infrastructures exert specific constraints on the mobile Internet user. We present the results of a study of the interactional complexities of a commuter reading WAP enabled devices. The WAP user is observed ethnographically and filmed while cell phone identification periodically gives information about the interviewee's physical location, indicating where and when the usability of a WAP device becomes difficult. In which way do communication and transport infrastructures intersect and how do people negotiate this intersection?

The rapid growth of mobile traffic and the emergence of advanced mobile services and infrastructures are shifting significant attention toward the cellular network back-hauling infrastructure. At this network segment, there is a growing interest in understanding spatio-temporal mobile traffic distributions at different network levels, in order to better define flexible networking solutions for forthcoming smart 5G infrastructures including, for instance, mobile edge computing features. In this work we study these aspects and characterize the load on cellular access networks using real-world anonymized subscriber data, from the Lyon metropolitan area in France, providing statistical distribution to the research community. We find that the traffic distribution at Node-B level is best fit by a Weibull distribution, and that at the radio network aggregation it is best fit by a hybrid Weibull-Pareto distribution.

An intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory.

International audience; Wi-Fi offloading is one of the most effective approaches to relieve the cellular radio access from part of the burgeoning mobile demand. To date, Wi-Fi offloading has been mainly leveraged in limited contexts, such as home, office or campus environments. In this paper, we investigate the scaling properties of Wi-Fi offloading, by studying how it would perform on a much larger scope than those considered today. To that end, we consider a real-world citywide scenario, built on data about actual infrastructure deployments and mobile traffic demand, and observe which amount of traffic could be accommodated by the existing pervasive Wi-Fi access infrastructure, were it opened to mobile users. We find that more than 80% of the mobile traffic demand in a large urban area may be easily served by Wi-Fi access points, under a wide range of system settings.

International audience; Wi-Fi technology has always been an attractive solution for catering the increasing data demand in mobile networks because of the availability of Wi-Fi networks, the high bit rates they provide, and the lower cost of ownership. However, the legacy WiFi technology lacks of seamless interworking between Wi-Fi and mobile cellular networks on the one hand, and between Wi-Fi hotspots on the other hand. Nowadays, the recently released Wi-Fi Certified Passpoint Program provides the necessary control-plane for these operations. Service providers can henceforth look to such Wi-Fi systems as a viable way to seamlessly offload mobile traffic and deliver added-value services, so that subscribers no longer face the frustration and aggravation of connecting to Wi-Fi hotspots. However, the technology being rather recent, we are not aware of public studies at the state of the art documenting the achievable gain in real mobile networks. In this paper, we evaluate the capacity and energy saving gain that one can get by offloading cellular data traffic over Passpoint hotspots as a function of different hotspot placement schemes and of access point selection policies (two enabled by the Passpoint control-plane and one independent of it). We compare the policies using real mobile data from the Orange network in Paris. We show that offloading using Passpoint control-plane information can grant up to 15% capacity gain and 13% energy saving gain with respect to Passpoint-agnostic ones based on signal quality information. As of placement strategy, installing Passpoint hotspots in the outer annulus of the macrocell coverage grants the maximum capacity gain.

Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France. A new pattern aiming to discover connections between regions is presented.The C-pattern is based on spatiotemporal aggregations of presence.It overcomes typical completeness limitations of CDR data due to the sample rate.We have developed an algorithm to extract these patterns efficiently.We have presented three different case studies using two different granularities.

Communication mediated by various technologies (from ordinary mail to today's Information and Communication Technologies (ICT)) provides important evidence for the study of social networks. Given that networks generate the possibility of interpersonal communication, data on technology use can provide important information on sociability. However, it is also true that personal networks not only shape, but also are shaped by technological means for communication, since these entail the re-constituting of social ties and the re-drawing of social boundaries. We use material from empirical studies carried out over the last 3 years to develop our hypothesis of the way forms of relationship change with technology. In particular, we try to understand the relationship between social networks (a set of social ties possessing one or more relational dimensions), exchanges between actors (made up of a succession of embodied gestures and language acts) and the various technical means for communication available today, which enable an exchange to be completed. Each of these three poles poses constraints on interaction, and provides resources for it, and thus all three shape the form relational practices take. Empirical data show how technological means of communication allow people to re-negotiate the constraints of individual time rhythms, and of who one communicates with. They also illustrate how the relational economy (and power) is affected by the deployment of communication technologies. Tools of communication provide new resources to negotiate individual timetables and social exchanges, making it possible to adjust roles, hierarchies and forms of power in relational economies. We argue that the general change observed over the last 20 years is from established roles to mutual reachability. The traditional communication model, where tele-communication is used to connect people who are physically separated from each other, is gradually being supplanted with a new pattern of “connected presence”. In this new mode other people are telephoned, “SMSed”, seen and mailed in alternated way and small gestures or signs of attention are at least as important as the message content itself.

Big Data offer nowadays the potential capability of creating a digital nervous system of our society, enabling the measurement, monitoring and prediction of relevant aspects of socio-economic phenomena in quasi real time. This potential has fueled, in the last few years, a growing interest around the usage of Big Data to support official statistics in the measurement of individual and collective economic well-being. In this work we study the relations between human mobility patterns and socioeconomic development. Starting from nation-wide mobile phone data we extract a measure of mobility volume and a measure of mobility diversity for each individual. We then aggregate the mobility measures at municipality level and investigate the correlations with external socio-economic indicators independently surveyed by an official statistics institute. We find three main results. First, aggregated human mobility patterns are correlated with these socio-economic indicators. Second, the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits the strongest correlation with the external socio-economic indicators. Third, the volume of mobility and the diversity of mobility show opposite correlations with the socioeconomic indicators. Our results, validated against a null model, open an interesting perspective to study human behavior through Big Data by means of new statistical indicators that quantify and possibly "nowcast" the socio-economic development of our society.

International audience; Mobile phone operators produce enormous amounts of data. In this paper we present applications performed with a dataset (communication events + handover and Location Area Up-date) collected by the operator Orange from 31 March to 11 April 2009 for the whole Paris Region. Trips are deduced from the spatio-temporal trajectory of devices through a hypothesis of stationarity within a Location Area in order to define activities. Trips are then aggregated in an origin-destination matrix which is compared with traditional data (census data and household travel survey).

The appearance of large geolocated communication datasets has recently increased our understanding of how social networks relate to their physical space. However, many recurrently reported properties, such as the spatial clustering of network communities, have not yet been systematically tested at different scales. In this work we analyze the social network structure of over 25 million phone users from three countries at three different scales: country, provinces and cities. We consistently find that this last urban scenario presents significant differences to common knowledge about social networks. First, the emergence of a giant component in the network seems to be controlled by whether or not the network spans over the entire urban border, almost independently of the population or geographic extension of the city. Second, urban communities are much less geographically clustered than expected. These two findings shed new light on the widely-studied searchability in self-organized networks. By exhaustive simulation of decentralized search strategies we conclude that urban networks are searchable not through geographical proximity as their country-wide counterparts, but through an homophily-driven community structure., New England University Transportation Center (Year 23 Grant), King Abdulaziz City of Science and Technology (Saudia Arabia). Center for Complex Engineering Systems, MIT-Accenture Alliance, Orange Spain (France Telecom Group), Fundacion Caja Madrid (Spain), Spanish Ministry of Economy and Competitiveness (MINECO) (Grant MTM2012-39101)

International audience; Wi-Fi technology has always been an attractive solution for catering the increasing data demand in mobile networks because of the availability of Wi-Fi networks, the high bit rates they provide, and the lower cost of ownership. However, the legacy WiFi technology lacks of seamless interworking between Wi-Fi and mobile cellular networks on the one hand, and between Wi-Fi hotspots on the other hand. Nowadays, the recently released Wi-Fi Certified Passpoint Program provides the necessary control-plane for these operations. Service providers can henceforth look to such Wi-Fi systems as a viable way to seamlessly offload mobile traffic and deliver added-value services, so that subscribers no longer face the frustration and aggravation of connecting to Wi-Fi hotspots. However, the technology being rather recent, we are not aware of public studies at the state of the art documenting the achievable gain in real mobile networks. In this paper, we evaluate the capacity gain that one can get by offloading cellular data traffic over Wi-Fi Passpoint hotspots as a function of different hotspot placement schemes and of access point selection policies (two enabled by the Passpoint control-plane and one independent of it). We compare the policies using real mobile data from the Orange network in Paris. We show that offloading using Passpoint control-plane information can grant up to 15% gain with respect to Passpoint-agnostic ones based on signal quality information. As of placement strategy, installing Passpoint hotspots in the outer annulus of the macrocell coverage grants the maximum gains.

International audience; —A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at the base station and backhauling network level. Intuitively, an adaptive placement of content and computing resources in the most crowded regions can grant important traffic offloading, improve network efficiency and user quality of experience. In this paper we document the content consumption in the Orange cellular network for the Paris metropolitan area, from spatial and application-level extensive analysis of real data from a few million users, reporting the experimental distributions. In this scope, we propose a hotspot cell estimator computed over user's mobility metrics and based on linear regression. Evaluating our estimator on real data, it appears as an excellent hotspot detection solution of cellular and backhauling network management. We show that its error strictly decreases with the cell load, and it is negligible for reasonable hotspot cell load upper thresholds. We also show that our hotspot estimator is quite scalable against mobility data volume and against time variations.

Human mobility is differentiated by time scales. While the mechanism for long time scales has been studied, the underlying mechanism on the daily scale is still unrevealed. Here, we uncover the mechanism responsible for the daily mobility patterns by analysing the temporal and spatial trajectories of thousands of persons as individual networks. Using the concept of motifs from network theory, we find only 17 unique networks are present in daily mobility and they follow simple rules. These networks, called here motifs, are sufficient to capture up to 90 per cent of the population in surveys and mobile phone datasets for different countries. Each individual exhibits a characteristic motif, which seems to be stable over several months. Consequently, daily human mobility can be reproduced by an analytically tractable framework for Markov chains by modelling periods of high-frequency trips followed by periods of lower activity as the key ingredient., Volkswagen Foundation, NEC Corporation (Fund), Massachusetts Institute of Technology (Solomon Buchsbaum Research Fund), New England University Transportation Center (Year 23 grant)

Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method. Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area. Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility. Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.

In this paper we present a method for describing how a node of a given graph is connected to the network. We also propose a method for grouping nodes into clusters based on the structure of the network in which they are embedded, so on the description provided by the first method. We apply these methods to a mobile phone communications network. When confronting the obtained clusters of individuals to their age and to their intensity of communication, the results are quite promising: the two measures are correlated to the social network cluster. We finish by providing a typology of the mobile phone users based on social network cluster, communication intensity and age.

International audience; A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at base station and back-hauling network level. In order to answer these questions, in this paper we document the content consumption in Orange cellular network for Paris metropolitan area. From spatial and application-level extensive analysis of real data, we numerically and statistically quantify the geographical distribution of content consumption with per-service classifications. We provide experimental statistical distributions usable for further research in the area.

In this paper we introduce a new metric to estimate the basic properties of displacements: mobility intensity (speed-like measure) and uncertainty. We use mobile phone Call Detail Records from technical GSM network probes. A spatiotemporal analysis of antennas activity and user mobility is proposed using these indicators.

In this work we use anonymous telecommunication data to investigate how migration influences the dynamics of mobile social networks. We analyze the evolution of social ties for an entire country over a period of 11 months, examining: tie strength, sociality level, distance of ties, and persistence of ties. We find changes in tie strengths pre and post migration, specifically at six months before and a month after migration and discover that on average it takes approximately seven to eight months to restructure ones social network at a new residential location following migration. Moreover, the strongest ties such as those between families and close friends appear to be independent of migration. These ties continue to persist at nearly constant rates, in stark contrast to the weaker ties whose persistence rates drop significantly. Finally, we quantify the interdependency between the persistence rate, social strength, and migration distance, as a result, we are able to estimate how likely a given tie would persist, given the pre-migration social strength and distance of migration., Volkswagen of America. Electronic Research Lab, AT&T Foundation, National Science Foundation (U.S.), MIT-Portugal Program, SENSEable City Laboratory Consortium

In this study we analyze one year of anonymized telecommunications data for over one million customers from a large European cellphone operator, and we investigate the relationship between people's calls and their physical location. We discover that more than 90% of users who have called each other have also shared the same space (cell tower), even if they live far apart. Moreover, we find that close to 70% of users who call each other frequently (at least once per month on average) have shared the same space at the same time - an instance that we call co-location. Co-locations appear indicative of coordination calls, which occur just before face-to-face meetings. Their number is highly predictable based on the amount of calls between two users and the distance between their home locations - suggesting a new way to quantify the interplay between telecommunications and face-to-face interactions., AT & T Foundation, National Science Foundation (U.S.) (grant number 0735956), Singapore-MIT Alliance for Research and Technology, General Electric Company, Volkswagen Foundation, IBM Research, Orange Labs (France), France. Société Nationale des Chemins de fer français, Ente nazionale per l'energia elettrica

In this paper, we analyze statistical properties of a communication network constructed from the records of a mobile phone company. The network consists of 2.5 million customers that have placed 810 millions of communications (phone calls and text messages) over a period of 6 months and for whom we have geographical home localization information. It is shown that the degree distribution in this network has a power-law degree distribution $k^{-5}$ and that the probability that two customers are connected by a link follows a gravity model, i.e. decreases like $d^{-2}$, where $d$ is the distance between the customers. We also consider the geographical extension of communication triangles and we show that communication triangles are not only composed of geographically adjacent nodes but that they may extend over large distances. This last property is not captured by the existing models of geographical networks and in a last section we propose a new model that reproduces the observed property. Our model, which is based on the migration and on the local adaptation of agents, is then studied analytically and the resulting predictions are confirmed by computer simulations., Comment: 17 pages, 8 figures

The paper is based on a study on the uses of the telephone in 317 French homes. Detailed billing data over four months were collected and compared with interviews concerning each regular phone user in the household. We focus here on the correlation between the observed duration of phone calls and the gender of callers and receivers. The reasons why women telephone more are discussed and related to the gender homophily in telephone networks. Data also show that the duration of phone calls depends on the receiver's gender. We suggest that politeness rules may govern the telephone call, did we argue that these civility rules explain in part why the receiver's gender exerts the greatest effect on how the call is managed and on its overall duration. We propose to explore the hypothesis that telephone calls have an interactional format similar to that of a visit, but that status differences between caller and receiver may influence the unfolding of interactions between men and women through the caller's adjustment of his or her interaction style to the receiver's gender.