1,026 research outputs found
Weak nodes detection in urban transport systems: Planning for resilience in Singapore
The availability of massive data-sets describing human mobility offers the
possibility to design simulation tools to monitor and improve the resilience of
transport systems in response to traumatic events such as natural and man-made
disasters (e.g. floods terroristic attacks, etc...). In this perspective, we
propose ACHILLES, an application to model people's movements in a given
transport system mode through a multiplex network representation based on
mobility data. ACHILLES is a web-based application which provides an
easy-to-use interface to explore the mobility fluxes and the connectivity of
every urban zone in a city, as well as to visualize changes in the transport
system resulting from the addition or removal of transport modes, urban zones,
and single stops. Notably, our application allows the user to assess the
overall resilience of the transport network by identifying its weakest node,
i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To
demonstrate the impact of ACHILLES for humanitarian aid we consider its
application to a real-world scenario by exploring human mobility in Singapore
in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic
An analytical framework to nowcast well-being using mobile phone data
An intriguing open question is whether measurements made on Big Data
recording human activities can yield us high-fidelity proxies of socio-economic
development and well-being. Can we monitor and predict the socio-economic
development of a territory just by observing the behavior of its inhabitants
through the lens of Big Data? In this paper, we design a data-driven analytical
framework that uses mobility measures and social measures extracted from mobile
phone data to estimate indicators for socio-economic development and
well-being. We discover that the diversity of mobility, defined in terms of
entropy of the individual users' trajectories, exhibits (i) significant
correlation with two different socio-economic indicators and (ii) the highest
importance in predictive models built to predict the socio-economic indicators.
Our analytical framework opens an interesting perspective to study human
behavior through the lens of Big Data by means of new statistical indicators
that quantify and possibly "nowcast" the well-being and the socio-economic
development of a territory
PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach
The problem of evaluating the performance of soccer players is attracting the
interest of many companies and the scientific community, thanks to the
availability of massive data capturing all the events generated during a match
(e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated
and widely accepted metric for measuring performance quality in all of its
facets. In this paper, we design and implement PlayeRank, a data-driven
framework that offers a principled multi-dimensional and role-aware evaluation
of the performance of soccer players. We build our framework by deploying a
massive dataset of soccer-logs and consisting of millions of match events
pertaining to four seasons of 18 prominent soccer competitions. By comparing
PlayeRank to known algorithms for performance evaluation in soccer, and by
exploiting a dataset of players' evaluations made by professional soccer
scouts, we show that PlayeRank significantly outperforms the competitors. We
also explore the ratings produced by {\sf PlayeRank} and discover interesting
patterns about the nature of excellent performances and what distinguishes the
top players from the others. At the end, we explore some applications of
PlayeRank -- i.e. searching players and player versatility --- showing its
flexibility and efficiency, which makes it worth to be used in the design of a
scalable platform for soccer analytics
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
The movements of individuals within and among cities influence key aspects of
our society, such as the objective and subjective well-being, the diffusion of
innovations, the spreading of epidemics, and the quality of the environment.
For this reason, there is increasing interest around the challenging problem of
flow generation, which consists in generating the flows between a set of
geographic locations, given the characteristics of the locations and without
any information about the real flows. Existing solutions to flow generation are
mainly based on mechanistic approaches, such as the gravity model and the
radiation model, which suffer from underfitting and overdispersion, neglect
important variables such as land use and the transportation network, and cannot
describe non-linear relationships between these variables. In this paper, we
propose the Multi-Feature Deep Gravity (MFDG) model as an effective solution to
flow generation. On the one hand, the MFDG model exploits a large number of
variables (e.g., characteristics of land use and the road network; transport,
food, and health facilities) extracted from voluntary geographic information
data (OpenStreetMap). On the other hand, our model exploits deep neural
networks to describe complex non-linear relationships between those variables.
Our experiments, conducted on commuting flows in England, show that the MFDG
model achieves a significant increase in the performance (up to 250\% for
highly populated areas) than mechanistic models that do not use deep neural
networks, or that do not exploit geographic voluntary data. Our work presents a
precise definition of the flow generation problem, which is a novel task for
the deep learning community working with spatio-temporal data, and proposes a
deep neural network model that significantly outperforms current
state-of-the-art statistical models
Human Mobility Modelling:Exploration and Preferential Return Meet the Gravity Model
AbstractModeling the properties of individual human mobility is a challenging task that has received increasing attention in the last decade. Since mobility is a complex system, when modeling individual human mobility one should take into account that human movements at a collective level influence, and are influenced by, human movement at an individual level. In this paper we propose the d-EPR model, which exploits collective information and the gravity model to drive the movements of an individual and the exploration of new places on the mobility space. We implement our model to simulate the mobility of thousands synthetic individuals, and compare the synthetic movements with real trajectories of mobile phone users and synthetic trajectories produced by a prominent individual mobility model. We show that the distributions of global mobility measures computed on the trajectories produced by the d-EPR model are much closer to empirical data, highlighting the importance of considering collective information when simulating individual human mobility
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