NASA has developed a capability for terminal area precision scheduling and spacing (TAPSS) to increase the use of fuel-efficient arrival procedures during periods of traffic congestion at a high-density airport. Sustained use of fuel-efficient procedures throughout the entire arrival phase of flight reduces overall fuel burn, greenhouse gas emissions and noise pollution. The TAPSS system is a 4D trajectory-based strategic planning and control tool that computes schedules and sequences for arrivals to facilitate optimal profile descents. This paper focuses on quantifying the efficiency benefits associated with using the TAPSS system, measured by reduction of level segments during aircraft descent and flight distance and time savings. The TAPSS system was tested in a series of human-in-the-loop simulations and compared to current procedures. Compared to the current use of the TMA system, simulation results indicate a reduction of total level segment distance by 50% and flight distance and time savings by 7% in the arrival portion of flight (~200 nm from the airport). The TAPSS system resulted in aircraft maintaining continuous descent operations longer and with more precision, both achieved under heavy traffic demand levels.

Graph theory is used to investigate three different problems arising in air traffic management. First, using a polynomial reduction from a graph partitioning problem, it is shown that both the airspace sectorization problem and its incremental counterpart, the sector combination problem are NP-hard, in general, under several simple workload models. Second, using a polynomial time reduction from maximum independent set in graphs, it is shown that for any fixed e, the problem of finding a solution to the minimum delay scheduling problem in traffic flow management that is guaranteed to be within n 1-e of the optimal, where n is the number of aircraft in the problem instance, is NP-hard. Finally, a problem arising in precision arrival scheduling is formulated and solved using graph reachability. These results demonstrate that graph theory provides a powerful framework for modeling, reasoning about, and devising algorithmic solutions to diverse problems arising in air traffic management.

A method is introduced for rapid prototyping and benchmarking of arrival sequencing and scheduling algorithms for air traffic management. The method has three main components: 1) algorithm models 2) recorded air traffic simulation data, and 3) a free software library that interfaces the algorithm models with the simulation output database. By accessing simulation data via a relational database, the method makes it possible to quickly implement prototype algorithms and then to evaluate them without a direct interface to the airspace simulator code. To illustrate this method, a basic first-come, firstserved arrival sequencing and scheduling algorithm was studied. Results are offered as benchmarks for comparison with future arrival sequencing and scheduling algorithms.

This paper evaluates the effect of Lateral NAVigation (LNAV) and Vertical NAVigation (NAV) equipage on the performance on time-based scheduling. The paper relies on two companion papers: (i) the first paper develops a high-fidelity simulations model of LNAV + VNAV, and (ii) the second paper develops a realistic spatio-temporally correlated model of wind uncertainty. Two different Monte Carlo simulation frameworks are used to evaluate the effect of LNAV + VNAV equipage on time-based-scheduling. The first Monte Carlo simulation framework is used to evaluate the time-of-arrival uncertainty for flights equipped with LNAV + VNAV capability. This Monte Carlo simulation uses a point-mass simulation model of the aircraft equipped with LNAV + VNAV and flying through spatio-temporally correlated wind uncertainty fields. The second Monte Carlo simulation evaluates the effect of LNAV + VNAV equipage on the performance of time-based scheduling. NASA's Stochastic Terminal Area Scheduling and Simulation (STASS) software is used for timebased scheduling and terminal area traffic simulations. Terminal-area traffic Monte Carlo simulations are conducted for 1.0x, 1.5x, and 2.0x demand ratio at San-Francisco International Airport (SFO), Los Angeles International Airport (LAX), and Dallas Fort Worth International Airport (DFW). Results illustrating the effect of LNAV + VNAV equipage level (0%, 25%, 50%, and 75%) on delay, throughput, and the number of separation violations are presented in the paper.

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