Tomas Singliar

MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS

Learning the behavior of traffic flow

• Broschiertes Buch

Produktbeschreibung

This thesis brings a collection of novel models and
methods that result from a new look at practical
problems in transportation through the prism of newly
available sensor data.

From this data, we build a model of traffic flow
inspired by macroscopic flow models. Unlike
traditional such models, our model deals with
uncertainty of measurement and unobservability of
certain important quantities and incorporates
on-the-fly observations more easily. Having a
predictive distribution of traffic state enables the
application of powerful decision-making machinery to
the traffic domain.

Secondly, a new method for detecting accidents and
other adverse events is described. Data collected
from highways enables us to bring supervised learning
approaches to incident detection. However, a major
hurdle to performance of supervised learners is the
quality of data which contains systematic biases
varying from site to site. We build a dynamic
Bayesian network framework that learns and rectifies
these biases, leading to improved supervised
detector performance with little need for manually
tagged data. The realignment method applies generally
to virtually all forms of labeled sequential data.

Produktdetails

• Verlag: VDM Verlag Dr. Müller
• Seitenzahl: 128
• Englisch
• Abmessung: 220mm x 150mm x 8mm
• Gewicht: 188g
• ISBN-13: 9783639171600
• ISBN-10: 3639171608
• Artikelnr.: 26570075

Autorenporträt

Tomas specializes in machine learning and anomaly detection,
especially by means of graphical probability models. He obtained
his PhD from University of Pittsburgh in 2008, authored papers
on inference in graphical models, modeling of large sensor data
sets and anomaly detection and served on program committees of
several major AI conferences.