Eric Horvitz, Johnson Apacible, Raman Sarin, Lin Liao
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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.
Keywords: Bayesian models of surprise, models of competency, statistical models of traffic flow and congestion.
In: E. Horvitz, J. Apacible, R. Sarin, and L. Liao (2005). Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service, Twenty-First Conference on Uncertainty in Artificial Intelligence, UAI-2005, Edinburgh, Scotland, July 2005.
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