  We present research on developing models that forecast traffic flow and
congestion in the Greater Seattle area. The research has led to the deployment
of a service named JamBayes, that is being actively used by over 2,500 users
via smartphones and desktop versions of the system. We review the modeling
effort and describe experiments probing the predictive accuracy of the models.
Finally, we present research on building models that can identify current and
future surprises, via efforts on modeling and forecasting unexpected
situations.
