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This study analyzes in-flight icing constraints and air traffic data in order to identify how those constraints translate into Air Traffic Management (ATM) impacts in the National Airspace System (NAS). The analysis includes major safety and efficiency issues NAS users consider when making decisions due to in-flight icing. Data suggest that in-flight icing is a terminal-area constraint with greatest impact when the icing constraint is on or near ground level; above-ground icing constraints can often be mitigated by directing traffic flows to pass under or around the icing constraint to and from terminal airports. Terminal-area icing does not impact en route traffic flow, but it can impact planning when a major airport is restricted by terminal-area icing. Statistical analysis developed in this study indicates that the most significant ATM impacts include delays, cancellations, diversions, airborne circular holding, and decreased airport arrival and departure rates.

Analysis of historical trajectories showed that when weather is impacting terminal operations, it is common for flights to deviate from published routes, even to the extent of using departure airspace for arrivals. This demonstrates that more flexible routing is needed in terminal airspace, especially when there are weather constraints. In this paper, we present an algorithm for the integrated design of dynamic arrival and departure weather avoidance routing within extended terminal airspace. This algorithm can serve as a strategic planning tool to aid air traffic controllers in managing terminal operations during weather events. Due to safety and efficiency considerations, arrival route structures are first designed and then modeled as constraints when designing the departure routes. The algorithm combines trajectory optimization with heuristics and takes into account human factors constraints, such as distance between merge points and number of merging flows per merge point. We demonstrate through fast-time simulation that dynamic weather avoidance routing can improve terminal operation efficiencies.

This paper describes an algorithmic approach for designing dynamic route structures for extended terminal airspace in the presence of convective weather. The algorithm combines trajectory optimization with heuristics to design efficient weather avoidance dynamic route structures. Optimal routing is performed for as far as 150 nautical miles from major airports. In support of Dynamic Airspace Configuration, factors such as distance between merge points, number of merging flows per merge point, and robustness of routes with respect to uncertainties in weather forecasts are explicitly considered. We present analysis of historical terminal operations that was conducted to identify suitable scenarios and also to gain insights and guidance for the design of dynamic route structure. We also present the algorithm details and experimental results.

Advanced airspace concepts have been proposed to automate separation services currently being provided by human controllers. Different concepts propose fundamentally different system architectures and roles for human operators. Investigating the relationship between safety and capacity could help the concept developers to identify critical points of failure and refine systems' architecture to reduce implementation cost and provide higher level of overall safety. In our previous work we developed a framework for safety-capacity analysis and phase transition. In this paper we present Safety Analysis Tool for Automated Airspace Concepts (SafeATAC) which is an automated tool with a graphical user interface that facilitates system-wide safety analysis, establishing safety-capacity relationships, and investigating phase transition. We discuss the SafeATAC framework and present sample analysis for candidate automation concepts. We also demonstrate how SafeATAC can be used to calculate the required reliability measures for different system components to achieve a target level of safety.

In this paper we analyze alternative near-term Traffic Flow Management Operational Incentives (OPI). These OPIs are part of a larger effort to help accelerate airlines’ equipage efforts with respect to NextGen avionics in the areas of Communication, Navigation, and Surveillance. We analyze alternative operational incentives that can be implemented through the slot allocation process in a Ground Delay Program. Potential incentives can either provide natural benefits through capacity increase, or redistribute ground delays in a zero-sum fashion in favor of equipped flights. We argue that, due to the complex nature of airline operations, quantifying the benefits of these incentives can be challenging. Furthermore, as airlines compete for limited resources in the National Airspace System, incentives may trigger different actions among airlines. Thus we propose a marginal analysis with respect to varying equipage mixtures to study the local and system-wide ramifications of possible equipage decisions made by airlines in reaction to each incentive. This analysis framework can aid policymakers in selecting the appropriate operational incentive mechanisms to yield desirable outcomes. ull realization of NextGen benefits to the National Airspace System (NAS) relies upon aircraft being equipped with certain technological systems. However, the equipage process has faced impediments that have slowed equipage rates below targets. These impediments include high costs of acquisition, retrofitting, and crew training. Furthermore, uncertainty regarding return on investment in terms of the time until benefits start to accrue, as well as the actual magnitude of the benefits, complicates the decision-making process for the airlines to equip. Moreover, due to the highly competitive nature of airline operations, unresolved issues, such as early adopter versus free rider , exacerbate the uncertainty among airlines in making the decision to equip their fleets. It is believed that a combination of financial 1 and operational incentives 2,3 can help accelerate the equipage process by alleviating some uncertainty regarding the realization of the benefits associated with equipage. Previous studies 2,4 have indicated that strategic-level Traffic Management Initiatives (TMI) such as Ground Delay Programs (GDP) and Airspace Flow Programs (AFP) can be used as a vehicle for Traffic Flow Management (TFM) Operational Incentives (OPI). Such incentives can be implemented by changing the resource allocation algorithms used to assign ground delays to flights. These alterations affect the distribution of benefits—in the form of delay reductions—among equipped and unequipped flights. The Joint Planning and Development Office (JPDO) offers an in-depth analysis of resource allocation and flight prioritization mechanisms in TMIs. 5 We classify TFM OPIs into two major categories based on how benefits are generated. The first category of incentives is associated with transitional benefits, in which equipage may not result in a near-term increase in capacity or throughput. The second category of incentives is associated with natural benefits, wherein increased capacity or throughput arises from the equipage and the consequent improvement in the aircraft performance.

Successful implementation of NextGen will require a critical mass of aircraft to be equipped with a range of avionics in the areas of communication, navigation, and surveillance. The equipage process has faced impediments that have slowed equipage rates below targets. It is believed that a package of financial and operational incentives can help accelerate the equipage process by incentivizing and facilitating the use of NextGen equipage. This paper focuses on Traffic Flow Management (TFM) Operational Incentives (OPIs) that can be implemented in the near-term. These incentives can help accelerate the equipage process by providing short-term benefits to early adopters until the full benefits of equipage materialize in the far term. Also, implementing equipage-aware Traffic Management Initiatives (TMIs) can enable near-term implementation of equipage-dependent concepts in a mixed-equipage environment. This, in turn, can reduce the airlines' uncertainty with respect to equipage return on investment. In this paper we conduct a high-level quantitative analysis to gain insight into the ramifications of TFM OPIs under a range of equipage levels.

*† ‡ § This study analyzes the Air Traffic Management (ATM) impacts of surface/terminal weather constraints at the 35 busiest airports in the National Airspace System (NAS). The degradation in airport departure and arrival rate performance is investigated during time periods where ceiling, visibility, surface winds, freezing precipitation, and thunderstorms prevent clear weather throughput. Using cumulative distribution functions (CDFs), we compare airport performance during time periods when weather hazards are present with clear weather baseline performance. Comparing against the clear weather baseline as well as across weather hazard types allows us to rank the effects of various weather hazards. Weighting such results by the frequency of occurrence of the weather event allows us to identify the most costly weather hazards in the NAS for terminal area operations. Such information allows us to focus future ATM investments on those technologies that can best mitigate the most significant weather hazards affecting the NAS. All comparisons are performed with 2009 operational ATM data.

*† This study analyzes aviation in-flight icing constraints and air traffic data in order to identify how those constraints translate into Air Traffic Management (ATM) impacts in the National Airspace System (NAS). A causality analysis aids in the creation of an in-flight icing impact translation model by specifying the link between the causes and the impacts that these constraints have on NAS resources. The causality analysis in this paper includes major safety and efficiency issues NAS users consider when making decisions due to in-flight icing. These impacts include cancellations, diversions, delays, holding, altitude selection, and decreased airport arrival and departure rates. The focus of this paper is to quantify the impacts that are identified in the causality analysis. Statistical methods are applied to check, when possible, that the causes and impacts have a statistically significant correlation.