The Next Generation Air Transportation System (NextGen) is expected to bring about major improvements in both airspace design and utilization. One element of NextGen, Dynamic Airspace Configuration (DAC), is proposed as a means to facilitate substantially more efficient airspace capacity management. A new metric or set of metrics is required for analyzing future airspace design concepts like DAC. These metrics are likely to replace the current operational index of workload (i.e., Monitor Alert Parameter, or MAP). The new metric(s) should be able to accommodate various operational concepts and their associated airspace designs. Toward this end, we have developed a multi-component metric, Simplified Dynamic Density (SDD), whose component weightings can be adjusted to the relevant situational characteristics created by various operational concepts. The value of this metric, as evidenced by the current study, is that (a) it can be computed from a single input file containing flight-planned trajectories such as ETMS "FZ" records; (b) in addition to calibration against controller workload in real-time human-in-the-loop simulations, it can be "self-calibrated" using historical data and certain reasonable assumptions on sector workload in today's airspace; and (c) it can be applied successfully to trigger DAC actions, thereby optimizing airspace use. With continued development and use of SDD it should be possible for the research community to better understand NextGen concepts and their impact on the workload of the ATM operational community.

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The Airspace Playbook is a set of airspace configuration scenarios that augment the National Severe Weather Playbook rerouting scenarios. Each rerouting "play" would be accompanied with a candidate dynamic airspace reconfiguration. Typically, this would involve adjustments of sector boundaries in increments of existing sector components. Occasionally, these reconfigurations may combine two or more sectors into larger sectors or form dedicated smaller sectors to handle specific flows. The Airspace Playbook concept was examined using the distribution of the average sector occupancy counts during good-weather days and during weather-impacted days when specific rerouting "plays" were used. The sector occupancy count metric is used as an initial proxy for sector workload. Accordingly, airspace re-sectorization "plays" that accompany the rerouting "plays" are developed. The study shows that applying these dynamic sector boundary adjustments that complement reroutes can produce more evenly distributed occupancy counts in affected sectors and also lower their peak traffic load, an indicator of improved workload distribution.

In this paper, we present a new predictive model for estimating airport delay using data from weather forecast products. We use the well established Weather Impacted Traffic Index (WITI) toolset and metric. The latter quantifies the "front-end" impact of weather and traffic demand on the NAS and is well correlated with NAS delays, which makes WITI a reasonably good high-level model of NAS performance. WITI-FA ("Forecast Accuracy") is the forecast-weather counterpart to WITI: it can use various convective forecast products, as well as Terminal Area Forecast (TAF), and quantify forecast weather impact on the NAS. We show how these models can be refined and re-oriented toward predictive capability. First, instead of using just three WITI components, we break down the weather impacts by type, e.g. wind, snow, low ceilings, enroute thunderstorms, volume, etc -twelve components in total. Second, instead of using a NASwide WITI model, we "zoom in" on individual airports. The model is calibrated to minutes of delay for a given airport on an hourly basis. Having trained the model using historical airport performance and actual weather / scheduled traffic data, we then apply it in a predictive mode. The paper contains multiple examples and comparisons of predicted vs. actual delays at major airports under various weather conditions. In addition to predicting delays, the model can be used as a decision support tool. If predicted delays are too high, WITI can be run in what-if mode to gauge demand reduction, guaranteeing sustainable delays in adverse weather conditions. This could also be helpful to airlines when they need to decide on the amount of flight cancellations. Lastly, our airport delay predictor model can be used to compare the efficacy of different weather forecast products.

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