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
Big Data for Social Sciences: Measuring patterns of human behavior through large-scale mobile phone data
Through seven publications this dissertation shows how anonymized mobile
phone data can contribute to the social good and provide insights into human
behaviour on a large scale. The size of the datasets analysed ranges from 500
million to 300 billion phone records, covering millions of people. The key
contributions are two-fold:
1. Big Data for Social Good: Through prediction algorithms the results show
how mobile phone data can be useful to predict important socio-economic
indicators, such as income, illiteracy and poverty in developing countries.
Such knowledge can be used to identify where vulnerable groups in society are,
reduce economic shocks and is a critical component for monitoring poverty rates
over time. Further, the dissertation demonstrates how mobile phone data can be
used to better understand human behaviour during large shocks in society,
exemplified by an analysis of data from the terror attack in Norway and a
natural disaster on the south-coast in Bangladesh. This work leads to an
increased understanding of how information spreads, and how millions of people
move around. The intention is to identify displaced people faster, cheaper and
more accurately than existing survey-based methods.
2. Big Data for efficient marketing: Finally, the dissertation offers an
insight into how anonymised mobile phone data can be used to map out large
social networks, covering millions of people, to understand how products spread
inside these networks. Results show that by including social patterns and
machine learning techniques in a large-scale marketing experiment in Asia, the
adoption rate is increased by 13 times compared to the approach used by
experienced marketers. A data-driven and scientific approach to marketing,
through more tailored campaigns, contributes to less irrelevant offers for the
customers, and better cost efficiency for the companies.Comment: 166 pages, PHD thesi
Routine pattern discovery and anomaly detection in individual travel behavior
Discovering patterns and detecting anomalies in individual travel behavior is
a crucial problem in both research and practice. In this paper, we address this
problem by building a probabilistic framework to model individual
spatiotemporal travel behavior data (e.g., trip records and trajectory data).
We develop a two-dimensional latent Dirichlet allocation (LDA) model to
characterize the generative mechanism of spatiotemporal trip records of each
traveler. This model introduces two separate factor matrices for the spatial
dimension and the temporal dimension, respectively, and use a two-dimensional
core structure at the individual level to effectively model the joint
interactions and complex dependencies. This model can efficiently summarize
travel behavior patterns on both spatial and temporal dimensions from very
sparse trip sequences in an unsupervised way. In this way, complex travel
behavior can be modeled as a mixture of representative and interpretable
spatiotemporal patterns. By applying the trained model on future/unseen
spatiotemporal records of a traveler, we can detect her behavior anomalies by
scoring those observations using perplexity. We demonstrate the effectiveness
of the proposed modeling framework on a real-world license plate recognition
(LPR) data set. The results confirm the advantage of statistical learning
methods in modeling sparse individual travel behavior data. This type of
pattern discovery and anomaly detection applications can provide useful
insights for traffic monitoring, law enforcement, and individual travel
behavior profiling
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically alerting anomalies in their early stage or even predicting anomalies before happening is of great value for populations. Recently, data-driven urban anomaly analysis frameworks have been forming, which utilize urban big data and machine learning algorithms to detect and predict urban anomalies automatically. In this survey, we make a comprehensive review of the state-of-the-art research on urban anomaly analytics. We first give an overview of four main types of urban anomalies, traffic anomaly, unexpected crowds, environment anomaly, and individual anomaly. Next, we summarize various types of urban datasets obtained from diverse devices, i.e., trajectory, trip records, CDRs, urban sensors, event records, environment data, social media and surveillance cameras. Subsequently, a comprehensive survey of issues on detecting and predicting techniques for urban anomalies is presented. Finally, research challenges and open problems as discussed.Peer reviewe
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