5,937 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
The path inference filter: model-based low-latency map matching of probe vehicle data
We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.Comment: Preprint, 23 pages and 23 figure
Mobile telephony - cooperation and value-added are key to further success
The current problems in mobile telephony are leading critics to make overly pessimistic predictions that 3G – the third-generation mobile phone system – will never become profitable. However, the resulting calls not to introduce 3G and instead directly back alternative wireless technologies (e.g. WLAN) are a step too far. Ultimately, a profit- oriented service can only create significant value-added with a mix of both 3G and WLAN technologies. It is notable that no attractive broadband-dependent applications have emerged as yet. The typical user is only interested in the value-added the application provides, not the underlying wireless technology. Although mobile telephony remains one of the most dynamic areas of the economy, euphoria is misplaced. Advanced wireless technologies will on no account become profitable before the start of the next decade. But even that is not a given; this will challenge the entrepreneurial spirit of network operators, mobile terminal manufacturers and service providers alike.ICT, IT, mobile, telephony, UMTS, WLAN
A World That Counts: Mobilising The Data Revolution For Sustainable Development
This report sets out the main opportunities and risks presented by the data revolution for sustainable development. Seizing these opportunities and mitigating these risks requires active choices, especially by governments and international institutions. Without immediate action, gaps between developed and developing countries, between information-rich and information-poor people, and between the private and public sectors will widen, and risks of harm and abuses of human rights will grow
The use of social networking technology in the promotion and scaling up of complex global health initiatives
Western medicine has a long tradition of humanitarian service in low resource countries and in crisis and disaster situations. However, advances in social network technology have dramatically changed the manner in which global health services are delivered. A new generation of healthcare professionals, modeled as social entrepreneurs, utilizing collaborative and nonprofit models is establishing relationships with healthcare professionals in host countries, to actively track early disease detection, scaling up of services and research. Oftentimes, technology allows healthcare professionals to contribute to these efforts remotely and without detracting from their routine clinical work as well as facilitating more flexible pathways for global health training in
postgraduate education. This paper examines the limitations and opportunities for the utilization of social networking technology, including health care workers as social entrepreneurs, in early disease detection and in scaling up of services and research.
Key words: global healthcare entrepreneurs, global burden of disease, social network technology, low resource countries, scaling up, capacity building, social enterpris
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
Terabit communications – tasks, challenges, and the impact of disruptive technologies
Throughout history, effective communication has been of THE most critical importance to all civilisations, the means employed being underpinned and enabled by the greatest scientific breakthroughs of the age. Today we live in an information age and consequently there is a growing need to send vast amounts of data both securely and at the shortest time possible across the globe. However, to keep pace with this demand it is critical that the capacity of future communication networks is able to perform accordingly. However it is an open secret that to achieve this is becoming an increasingly difficult task. In this paper we explore key technological milestones and breakthroughs that have enabled to support rapidly the growing demand for data. This will be followed by a discussion of the drivers of this demand, the socio-political consequences of this development, and the technical challenges we must overcome if demand is to be met into the future. These technical challenges encompass issues of CMOS scalability, power consumption, and data centres & network switching abilities
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