Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service

2012/07/04 by Eric Horvitz, Eric J. Horvitz, Johnson Apacible +6
Computer Science · Engineering · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Data Management and Algorithms #Data Visualization and Analytics #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Traffic Prediction and Management Techniques #cs.AI #physics.soc-ph

paper · pdf · doi:10.48550/arxiv.1207.1352

Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)

arxiv created 2012/07/04 · openalex publication_date 2012/07/04 · arxiv updated 2012/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Display of Information for Time-Critical Decision Making
• Staged Mixture Modelling and Boosting
• A Bayesian Approach to Learning Bayesian Networks with Local Structure

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