68 research outputs found
MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics
Smart mobility proved to be an important but challenging component of the smart
cities paradigm. The increased urbanization and the advent of sharing economy require
a complete digitalisation of the way travellers interact with the mobility services.
New sharing mobility services and smart transportation models are emerging as partial
solutions for solving some tra c problems, improve the resource e ciency and reduce
the environmental impact. The high connectivity between travellers and the sharing
services generates enormous quantity of data which can reveal valuable knowledge and
help understanding complex travel behaviour. Advances in data science, embedded
computing, sensing systems, and arti cial intelligence technologies make the development
of a new generation of intelligent recommendation systems possible. These
systems have the potential to act as intelligent transportation advisors that can o er
recommendations for an e cient usage of the sharing services and in
uence the travel
behaviour towards a more sustainable mobility. However, their methodological and
technological requirements will far exceed the capabilities of today's smart mobility
systems.
This dissertation presents a new data-driven approach for mobility analysis and travel
behaviour pro ling for smart mobility services. The main objective of this thesis is
to investigate how the latest technologies from data science can contribute to the
development of the next generation of mobility recommendation systems.
Therefore, the main contribution of this thesis is the development of new methodologies
and tools for mobility analysis that aim at combining the domain of transportation
engineering with the domain of data science. The addressed challenges are derived from
speci c open issues and problems in the current state of the art from the smart mobility
domain. First, an intelligent recommendation system for sharing services needs a
general metric which can assess if a group of users are compatible for speci c sharing
solutions. For this problem, this thesis presents a data driven indicator for collaborative
mobility that can give an indication whether it is economically bene cial for a group
of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing
mobility scenarios involve a high number of users and big data that must be handled by
capable modelling frameworks and data analytic platforms. To tackle this problem, a
suitable meta model for the transportation domain is created, using the state of the art
multi-dimensional graph data models, technologies and analytic frameworks. Thirdly,
the sharing mobility paradigm needs an user-centric approach for dynamic extraction
of travel habits and mobility patterns. To address this challenge, this dissertation
proposes a method capable of dynamically pro ling users and the visited locations in
order to extract knowledge (mobility patterns and habits) from raw data that can be
used for the implementation of shared mobility solutions. Fourthly, the entire process of
data collection and extraction of the knowledge should be done with near no interaction
from user side. To tackle this issue, this thesis presents practical applications such
as classi cation of visited locations and learning of users' travel habits and mobility
patterns using historical and external contextual data
Data Epistemologies / Surveillance and Uncertainty
Data Epistemologies studies the changing ways in which ‘knowledge’ is defined, promised, problematised, legitimated vis-á-vis the advent of digital, ‘big’ data surveillance technologies in early twenty-first century America. As part of the period’s fascination with ‘new’ media and ‘big’ data, such technologies intersect ambitious claims to better knowledge with a problematisation of uncertainty. This entanglement, I argue, results in contextual reconfigurations of what ‘counts’ as knowledge and who (or what) is granted authority to produce it – whether it involves proving that indiscriminate domestic surveillance prevents terrorist attacks, to arguing that machinic sensors can know us better than we can ever know ourselves.
The present work focuses on two empirical cases. The first is the ‘Snowden Affair’ (2013-Present): the public controversy unleashed through the leakage of vast quantities of secret material on the electronic surveillance practices of the U.S. government. The second is the ‘Quantified Self’ (2007-Present), a name which describes both an international community of experimenters and the wider industry built up around the use of data-driven surveillance technology for self-tracking every possible aspect of the individual ‘self’. By triangulating media coverage, connoisseur communities, advertising discourse and leaked material, I examine how surveillance technologies were presented for public debate and speculation.
This dissertation is thus a critical diagnosis of the contemporary faith in ‘raw’ data, sensing machines and algorithmic decision-making, and of their public promotion as the next great leap towards objective knowledge. Surveillance is not only a means of totalitarian control or a technology for objective knowledge, but a collective fantasy that seeks to mobilise public support for new epistemic systems. Surveillance, as part of a broader enthusiasm for ‘data-driven’ societies, extends the old modern project whereby the human subject – its habits, its affects, its actions – become the ingredient, the raw material, the object, the target, for the production of truths and judgments about them by things other than themselves
The 8th International Conference on Time Series and Forecasting
The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields
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