  Intelligent Transportation Systems (ITS) use data and information technology
to improve the operation of our transportation network. ITS contributes to
sustainable development by using technology to make the transportation system
more efficient; improving our environment by reducing emissions, reducing the
need for new construction and improving our daily lives through reduced
congestion. A key component of ITS is traveler information. The Oregon
Department of Transportation (ODOT) recently implemented a new traveler
information system on selected freeways to provide drivers with travel time
estimates that allow them to make more informed decisions about routing to
their destinations. The ODOT project aims to improve traffic flow and promote
efficient traffic movement, which can reduce emissions rates and improve air
quality. The new ODOT system is based on travel data collected from a
recently-increased set of sensors installed on its freeways. Our current
project investigates novel data cleaning methodologies and the integration of
those methodologies into the prediction of travel times. We use machine
learning techniques on our archive to identify suspect data, and calculate
revised travel times excluding this suspect data. We compare the resulting
travel time predictions to ground-truth data, and to predictions based on
simple, rule-based data cleaning. We report on the results of our study using
qualitative and quantitative methods.
