13,544 research outputs found
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
The pervasiveness and availability of mobile phone data offer the opportunity
of discovering usable knowledge about crowd behaviors in urban environments.
Cities can leverage such knowledge in order to provide better services (e.g.,
public transport planning, optimized resource allocation) and safer cities.
Call Detail Record (CDR) data represents a practical data source to detect and
monitor unusual events considering the high level of mobile phone penetration,
compared with GPS equipped and open devices. In this paper, we provide a
methodology that is able to detect unusual events from CDR data that typically
has low accuracy in terms of space and time resolution. Moreover, we introduce
a concept of unusual event that involves a large amount of people who expose an
unusual mobility behavior. Our careful consideration of the issues that come
from coarse-grained CDR data ultimately leads to a completely general framework
that can detect unusual crowd events from CDR data effectively and efficiently.
Through extensive experiments on real-world CDR data for a large city in
Africa, we demonstrate that our method can detect unusual events with 16%
higher recall and over 10 times higher precision, compared to state-of-the-art
methods. We implement a visual analytics prototype system to help end users
analyze detected unusual crowd events to best suit different application
scenarios. To the best of our knowledge, this is the first work on the
detection of unusual events from CDR data with considerations of its temporal
and spatial sparseness and distinction between user unusual activities and
daily routines.Comment: 18 pages, 6 figure
Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data
Despite the importance of predicting evacuation mobility dynamics after large
scale disasters for effective first response and disaster relief, our general
understanding of evacuation behavior remains limited because of the lack of
empirical evidence on the evacuation movement of individuals across multiple
disaster instances. Here we investigate the GPS trajectories of a total of more
than 1 million anonymized mobile phone users whose positions are tracked for a
period of 2 months before and after four of the major earthquakes that occurred
in Japan. Through a cross comparative analysis between the four disaster
instances, we find that in contrast with the assumed complexity of evacuation
decision making mechanisms in crisis situations, the individuals' evacuation
probability is strongly dependent on the seismic intensity that they
experience. In fact, we show that the evacuation probabilities in all
earthquakes collapse into a similar pattern, with a critical threshold at
around seismic intensity 5.5. This indicates that despite the diversity in the
earthquakes profiles and urban characteristics, evacuation behavior is
similarly dependent on seismic intensity. Moreover, we found that probability
density functions of the distances that individuals evacuate are not dependent
on seismic intensities that individuals experience. These insights from
empirical analysis on evacuation from multiple earthquake instances using large
scale mobility data contributes to a deeper understanding of how people react
to earthquakes, and can potentially assist decision makers to simulate and
predict the number of evacuees in urban areas with little computational time
and cost, by using population density information and seismic intensity which
can be observed instantaneously after the shock
Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network
Epidemiologic studies have established associations between various air
pollutants and adverse health outcomes for adults and children. Due to high
costs of monitoring air pollutant concentrations for subjects enrolled in a
study, statisticians predict exposure concentrations from spatial models that
are developed using concentrations monitored at a few sites. In the absence of
detailed information on when and where subjects move during the study window,
researchers typically assume that the subjects spend their entire day at home,
school or work. This assumption can potentially lead to large exposure
assignment bias. In this study, we aim to determine the distribution of the
exposure assignment bias for an air pollutant (ozone) when subjects are assumed
to be static as compared to accounting for individual mobility. To achieve this
goal, we use cell-phone mobility data on approximately 400,000 users in the
state of Connecticut during a week in July, 2016, in conjunction with an ozone
pollution model, and compare individual ozone exposure assuming static versus
mobile scenarios. Our results show that exposure models not taking mobility
into account often provide poor estimates of individuals commuting into and out
of urban areas: the average 8-hour maximum difference between these estimates
can exceed 80 parts per billion (ppb). However, for most of the population, the
difference in exposure assignment between the two models is small, thereby
validating many current epidemiologic studies focusing on exposure to ozone
CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network
Mobile phone data have recently become an attractive source of information
about mobility behavior. Since cell phone data can be captured in a passive way
for a large user population, they can be harnessed to collect well-sampled
mobility information. In this paper, we propose CT-Mapper, an unsupervised
algorithm that enables the mapping of mobile phone traces over a multimodal
transport network. One of the main strengths of CT-Mapper is its capability to
map noisy sparse cellular multimodal trajectories over a multilayer
transportation network where the layers have different physical properties and
not only to map trajectories associated with a single layer. Such a network is
modeled by a large multilayer graph in which the nodes correspond to
metro/train stations or road intersections and edges correspond to connections
between them. The mapping problem is modeled by an unsupervised HMM where the
observations correspond to sparse user mobile trajectories and the hidden
states to the multilayer graph nodes. The HMM is unsupervised as the transition
and emission probabilities are inferred using respectively the physical
transportation properties and the information on the spatial coverage of
antenna base stations. To evaluate CT-Mapper we collected cellular traces with
their corresponding GPS trajectories for a group of volunteer users in Paris
and vicinity (France). We show that CT-Mapper is able to accurately retrieve
the real cell phone user paths despite the sparsity of the observed trace
trajectories. Furthermore our transition probability model is up to 20% more
accurate than other naive models.Comment: Under revision in Computer Communication Journa
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