This report presents the results of analysis and characterization of uncertainty in traffic demand predictions using Enhanced Traffic Management System (ETMS) data and probabilistic representation of the predictions. Our previous research, described in two prior reports, was focused on analysis of aggregate 15-minute traffic demand predictions in ETMS, on improving the accuracy of these predictions and increasing the stability of the ETMS monitor/alert function, while not explicitly considering the uncertainty in predictions of flight events for individual flights. This study continues the previous one. It also focuses on uncertainty in traffic demand predictions, but, unlike the previous one, it explicitly considers uncertainty in individual flights' predictions for estimation of uncertainty in aggregate demand count predictions at National Airspace System (NAS) elements and for probabilistic representation of those predictions.

The purpose of this research is to examine the accuracy of predicted ETMS airport and sector counts, and to attempt to develop better prediction algorithms. The remainder of this report is in six sections. Section 1 Quality of Current Predictions Discussion of the accuracy of current flight predictions for a given location and 15-minute time interval. Section 2 New Models for Predicting the Number of Flights The development and calibration of new models for predicting the number of flights in a 15-minute time interval. Section 3 Testing Model 2 Discussion of the testing of one promising new model. Section 4 Relationship between Flight Predictions and Monitor/Alert Discussion of the relationship between flight predictions and alerts, along with some performance metrics for alerts. Section 5 Conclusion Section 6 Next Steps Section 7 References"