6,381 research outputs found
Creating Full Individual-level Location Timelines from Sparse Social Media Data
In many domain applications, a continuous timeline of human locations is
critical; for example for understanding possible locations where a disease may
spread, or the flow of traffic. While data sources such as GPS trackers or Call
Data Records are temporally-rich, they are expensive, often not publicly
available or garnered only in select locations, restricting their wide use.
Conversely, geo-located social media data are publicly and freely available,
but present challenges especially for full timeline inference due to their
sparse nature. We propose a stochastic framework, Intermediate Location
Computing (ILC) which uses prior knowledge about human mobility patterns to
predict every missing location from an individual's social media timeline. We
compare ILC with a state-of-the-art RNN baseline as well as methods that are
optimized for next-location prediction only. For three major cities, ILC
predicts the top 1 location for all missing locations in a timeline, at 1 and
2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all
compared methods). Specifically, ILC also outperforms the RNN in settings of
low data; both cases of very small number of users (under 50), as well as
settings with more users, but with sparser timelines. In general, the RNN model
needs a higher number of users to achieve the same performance as ILC. Overall,
this work illustrates the tradeoff between prior knowledge of heuristics and
more data, for an important societal problem of filling in entire timelines
using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table
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
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Recommended from our members
Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications
The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation.
Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban
infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability.
All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area
Digitizing Offline Shopping Behavior Towards Mobile Marketing
The proliferation of mobile technologies makes it possible for mobile advertisers to go beyond the real-time snapshot of the static location and contextual information about consumers. In this study, we propose a novel mobile advertising strategy that leverages full information on consumers’ offline moving trajectories. To evaluate the effectiveness of this strategy, we design a large-scale randomized field experiment in a large shopping mall in Asia based on 83,370 unique user responses for two weeks in 2014. We found the new mobile trajectory-based advertising is significantly more effective for focal advertising store compared to several existing baselines. It is especially effective in attracting high-income consumers. Interestingly, it becomes less effective during the weekend. This indicates closely targeted mobile ads may constrict consumer focus and significantly reduce the impulsive purchase behavior. Our finding suggests marketers should carefully design mobile advertising strategy, depending on different business contexts
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
Potential destination discovery for low predictability individuals based on knowledge graph
Travelers may travel to locations they have never visited, which we call
potential destinations of them. Especially under a very limited observation,
travelers tend to show random movement patterns and usually have a large number
of potential destinations, which make them difficult to handle for mobility
prediction (e.g., destination prediction). In this paper, we develop a new
knowledge graph-based framework (PDPFKG) for potential destination discovery of
low predictability travelers by considering trip association relationships
between them. We first construct a trip knowledge graph (TKG) to model the trip
scenario by entities (e.g., travelers, destinations and time information) and
their relationships, in which we introduce the concept of private relationship
for complexity reduction. Then a modified knowledge graph embedding algorithm
is implemented to optimize the overall graph representation. Based on the trip
knowledge graph embedding model (TKGEM), the possible ranking of individuals'
unobserved destinations to be chosen in the future can be obtained by
calculating triples' distance. Empirically. PDPFKG is tested using an anonymous
vehicular dataset from 138 intersections equipped with video-based vehicle
detection systems in Xuancheng city, China. The results show that (i) the
proposed method significantly outperforms baseline methods, and (ii) the
results show strong consistency with traveler behavior in choosing potential
destinations. Finally, we provide a comprehensive discussion of the innovative
points of the methodology
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