Tutorial title: Time-Evolving Data Streams
Learning and Short-Term Urban Traffic Flow Forecasting
Date: Sunday May 14th, 2017; Time 1:30 pm - 3:30 pm
Presenter: Prof. Francesco Masulli email: francesco<dot>masulli<at>unige<at>it
DIBRIS - Dept of Informatics,
Bioingengering, Robotics and Systems Engineering, University of Genova (ITALY)
and
Center for Biotechnology of
Temple University,
Philadelphia (PA, USA)
Abstract:
Data streams have arisen as a relevant topic during the last
decade.
In this tutorial we consider non-stationary data stream
clustering using
a possibilistic approach. The Graded Possibilistic
Clustering model
offers a way to evaluate “outlierness” through a natural
measure,
which is computed directly from the model. Both online and
batch
training approaches are considered, to provide different
trade-offs
between stability and speed of response to changes. The
proposed
approach is evaluated on a synthetic data set, for which the
ground
truth is available. Moreover, a real-time short-term urban
traffic
flow forecasting application is proposed, taking into
account both
spatial and temporal information. To this aim, we introduce
a Layered
Ensemble Model which combines Artificial Neural Networks and
Graded
Possibilistic Clustering models, obtaining in such a way an
accurate
forecaster of the traffic flow rates with outlier detection.
Experimentation has been carried out on two different data
sets. The
former consists on real UK motorway data and the latter is
obtained
from simulated traffic flow on a street network in Genoa
(Italy). The
proposed model for short-term traffic forecasting provides
promising
results and given its characteristics of outlier detection,
accuracy,
and robustness, and can be fruitful integrated in traffic
flow
management systems.
Presenter's
biography: Francesco Masulli is a Professor of
Computer Science with the Department of Informatics,
Bioingengering, Robotics and Systems Engineering
(DIBRIS) of the
University of Genoa (Italy) and Adjunct Professor at
the
Center for Biotechnology of Temple
University-Philadelphia (PA, USA).
He is lecturer of the courses on Fundamentals of
Computer Science,
Well-Being Technologies, Computational Intelligence
and Machine Learning at the University of
Genoa. He is the recipient of the 2008 Pattern
Recognition Society Award for
the paper "A survey of kernel and spectral methods
for
clustering". Author of more than 200 scientific
papers in
Clustering, Machine Learning, Neural Networks, Fuzzy
Systems,
Bioinformatics and Well-Being Technologies. He is also the President of Italian
Chapter of the IEEE-CIS and a Co-Chair
of the Special Interest Group on Bioinformatics and
Intelligence of the International Neural Network
Society.
Last updated 1 March 2017 by francesco<dot>masulli<at>unige<at>it