Trace- and Social-Based Modeling of Human Mobility Patterns

2012

Abstract This paper aims to analyze the GPS traces of 258 volunteers in order to obtain a better understanding of both the human mobility patterns and the mechanism. We report the regular and scaling properties of human mobility for several aspects, and importantly we identify its Levy flight characteristic, which is consistent with those from previous studies.

Understanding the patterns of mobility of individuals is crucial for a number of reasons, from city planning to disaster management. There are two common ways of quantifying the amount of travel between locations: by direct observations that often involve privacy issues, e.g., tracking mobile phone locations, or by estimations from models. Typically, such models build on accurate knowledge of the population size at each location. However, when this information is not readily available, their applicability is rather limited. As mobile phones are ubiquitous, our aim is to investigate if mobility patterns can be inferred from aggregated mobile phone call data alone. Using data released by Orange for Ivory Coast, we show that human mobility is well predicted by a simple model based on the frequency of mobile phone calls between two locations and their geographical distance. We argue that the strength of the model comes from directly incorporating the social dimension of mobility. Furthermore, as only aggregated call data is required, the model helps to avoid potential privacy problems. P eople travel and move for a variety of reasons, including social, economic, and political factors. While individuals may follow simple, recurrent patterns of movement, e.g., daily commuting, a more complex picture emerges when all trajectories of a population are assembled together 1 . Understanding the principles governing individual and collective movement is important for a number of reasons: for planning urban design 2 , for forecasting and avoiding traffic congestion 3 , for mitigating infectious disease 4-6 , and for contingency planning in extreme situations caused by disasters 7,8 . However, accurately determining the movement patterns in a population is cumbersome and costly, and involves privacy issues.

Physics Reports

Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between shortrange and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.

IEEE Access, 2022

Mobility is a critical element for understanding human contact networks. In many studies, the researchers use random processes to model human mobility. However, people do not move randomly in their environment. Their interactions do not depend only on spatial constraints but on their temporal, social, economic, and cultural activities. The topological structure of the physical and/or proximity contact networks depends, therefore, entirely on the mobility patterns. This paper performs an extensive comparative analysis of real-world temporal contact networks and synthetic networks based on influential mobility models. Results show that the various topological properties of most of the synthetic datasets depart from those observed in real-world contact networks because the randomness of some mobility parameters tends to move away from human contact properties. However, it appears that data generated using Spatio-Temporal Parametric Stepping (STEPS) mobility model reveals similarities with real temporal contact networks such as heavy-tailed distribution of contact duration, frequency of pairs of contacts, and the bursty phenomenon. These results pave the way for further improvement of mobility models to generate meaningful artificial contact networks. INDEX TERMS Temporal networks, contact networks, proximity networks, time varying graphs, human mobility networks, human dynamics.

2012

Opportunistic networks consist of mobile devices, carried by people in their everyday lives. They organize autonomously to exchange data with direct neighbors without the use of any infrastructural services. Since the devices are carried by humans, one of the main challenges to consider in opportunistic networks is the human mobility behavior. However, little work exists on how the social behavior of people drives their mobility behavior and how this context information can be systematically leveraged for opportunistic networking applications.

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...

We want to understand the impact of social relationships on mobility models that capture the essence of patterns in human movements. In this thesis, we develop data-driven models that demonstrate the effect of friendship on human mobility and capture how do humans move as a function of social ties. To model and analyze human mobility, we propose to use digital footprints left and shared on social networking media, analyze individual footprints to provide observations on how geoproximity impacts friendship and correlate to network communities, and use social relationships to model individual and population mobility. For an application, we apply our work in human mobility to supplement the state of the art research for mobile networks by simulating different mobility models to understand the effect of movements on network congestion. v

IEEE Commun. Mag, 2011

Mobile ad hoc networks (MANETs) enable communications between clouds of mobile devices without the need for a pre-existing infrastructure. One of their most interesting evolutions are Opportunistic Networks (OppNets), whose goal is to enable communication also in disconnected environments, where the general absence of an end-to-end path between the sender and the receiver impairs communication when legacy MANET networking protocols are used. The key idea of OppNets is that the mobility of nodes helps the delivery of messages, because it may connect, asynchronously in time, otherwise disconnected subnetworks. This is especially true for networks whose nodes are mobile devices (such as smartphones and tablets) carried by human users, which is the typical OppNets scenario. In such a network where the movements of the communicating devices mirror those of their owners, finding a route between two disconnected devices implies uncovering habits in human movements and patterns in their connectivity (i.e., frequencies of meetings, average duration of a contact, etc.), and exploiting them to predict future encounters. Therefore, there is a challenge in studying human mobility, specifically in its application to OppNets research. In this paper we review the state of the art in the field of human mobility analysis and present a survey of mobility models. We start from reviewing the most considerable findings regarding the nature of human movements, which we classify along the spatial, temporal, and social dimensions of mobility. We discuss the shortcomings of the existing knowledge about human movements and we extend it with the notion of predictability and patterns. We then survey existing approaches to mobility modeling and fit them into a taxonomy that provides the basis for a discussion on open problems and further directions for research on modeling human mobility.

2017

Nowadays we witness a rapid increase of people mobility as the world population has become more interconnected and is relying on faster transportation methods, simplified connections and shorter commuting times. Unveiling and understanding human mobility patterns have become a crucial issue to support decisions and prediction activities when managing the complexity of the today’s social organization. The strict connections between human mobility patterns, the planning, deployment and management of a variety of public and commercial services have fueled the rise of a vast research activity. Throughout this work, we are more interested and mainly focusing on urban mobility because here most of the human interactions take place and mobility has the greatest impact on management and optimization of public and commercial services. In this thesis, we provided a general framework for dealing with the modeling importance of locations from a per-user perspective and identified a few novel pr...

The massive amounts of geolocation data collected from mobile phone records has sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, focusing in particular in the cases of urban commute and major sports events. We illustrate how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location.