MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS
Learning the behavior of traffic flow
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This thesis brings a collection of novel models andmethods that result from a new look at practicalproblems in transportation through the prism of newlyavailable sensor data.From this data, we build a model of traffic flowinspired by macroscopic flow models. Unliketraditional such models, our model deals withuncertainty of measurement and unobservability ofcertain important quantities and incorporateson-the-fly observations more easily. Having apredictive distribution of traffic state enables theapplication of powerful decision-making machinery tothe traffic domain.Secondly, a new method for d...
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.
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.
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