183,278 research outputs found
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Mesoscopic structure and social aspects of human mobility
The individual movements of large numbers of people are important in many
contexts, from urban planning to disease spreading. Datasets that capture human
mobility are now available and many interesting features have been discovered,
including the ultra-slow spatial growth of individual mobility. However, the
detailed substructures and spatiotemporal flows of mobility - the sets and
sequences of visited locations - have not been well studied. We show that
individual mobility is dominated by small groups of frequently visited,
dynamically close locations, forming primary "habitats" capturing typical daily
activity, along with subsidiary habitats representing additional travel. These
habitats do not correspond to typical contexts such as home or work. The
temporal evolution of mobility within habitats, which constitutes most motion,
is universal across habitats and exhibits scaling patterns both distinct from
all previous observations and unpredicted by current models. The delay to enter
subsidiary habitats is a primary factor in the spatiotemporal growth of human
travel. Interestingly, habitats correlate with non-mobility dynamics such as
communication activity, implying that habitats may influence processes such as
information spreading and revealing new connections between human mobility and
social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table
(supporting information
Correlations Between Human Mobility and Social Interaction Reveal General Activity Patterns
A day in the life of a person involves a broad range of activities which are
common across many people. Going beyond diurnal cycles, a central question is:
to what extent do individuals act according to patterns shared across an entire
population? Here we investigate the interplay between different activity types,
namely communication, motion, and physical proximity by analyzing data
collected from smartphones distributed among 638 individuals. We explore two
central questions: Which underlying principles govern the formation of the
activity patterns? Are the patterns specific to each individual or shared
across the entire population? We find that statistics of the entire population
allows us to successfully predict 71\% of the activity and 85\% of the
inactivity involved in communication, mobility, and physical proximity.
Surprisingly, individual level statistics only result in marginally better
predictions, indicating that a majority of activity patterns are shared across
{our sample population}. Finally, we predict short-term activity patterns using
a generalized linear model, which suggests that a simple linear description
might be sufficient to explain a wide range of actions, whether they be of
social or of physical character
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