Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service
This work addresses traffic forecasting for urban commuters, but appears incremental as it builds on existing modeling approaches without claiming major breakthroughs.
The researchers developed traffic flow and congestion forecasting models for the Greater Seattle area, resulting in the deployment of JamBayes, a service used by over 2,500 users, and explored methods to predict unexpected traffic situations.
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.