Air Traffic Flow Management

Vossen, Thomas; Hoffman, Robert; Mukherjee, Avijit

doi:10.1007/978-1-4614-1608-1_7

Cited by 33 publications

(11 citation statements)

References 91 publications

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“…Heuristic 2 takes four iterations to converge, which performs slightly worst than first saturation heuristic (346.06 versus 331. 19). In this use case, the capacity in scenario 1 is strictly better than scenario 2, which is in turn better than scenario 1.…”

Section: Stochastic Model Comparisons In Terms Of System Delay Costsmentioning

confidence: 73%

“…Since the 1990s, the Federal Aviation Administration (FAA) has made significant changes in their air traffic flow management, moving from a centralized system to one called Collaborative Decision Making (CDM). Many decision support tools for air traffic managers and airline personnel are developed under this CDM paradigm [19]. Representative work differs in two aspects: the degree to which traffic iv managers can modify or revise flights' controlled departure times and the compatibility with current CDM software.…”

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confidence: 99%

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Centralized Disaggregate Stochastic Allocation Models for Collaborative Trajectory Options Program (CTOP)

Zhu

Wei

Hoffman³

et al. 2018

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

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As a new tool in the NextGen portfolio, the Collaborative Trajectory Options Programs (CTOP) combines multiple features from its forerunners including Ground Delay Program (GDP), Airspace Flow Program (AFP) and reroutes, and can manage multiple Flow Constrained Areas (FCAs) with a single program. A key research question in CTOP is how to set traffic flow rates under traffic demand and airspace capacity uncertainties. In this paper, we first investigate existing CTOP related stochastic optimization models and point out their roles in CTOP flow rate planning, and their advantages and disadvantages in terms of model flexibility, performance, practicality and Collaborative Decision Making (CDM) software compatibility, etc. CTOP FCA rate planning problem has been split into two steps: traffic flow rate optimization given demand estimation, and flow rate adaptation when flight rerouting is considered. Second, we discuss in detail a class of models called FCA-PCA (Potentially Constrained Area) models, which are extended from GDP models to solve the first step of the problem, and were considered promising as they are designed to be consistent with current CTOP software implementation. We will reveal one inherent shortcoming suffered by FCA-PCA models and show that how this deficiency can be addressed by the PCA model family. We will talk about the problems that prevent stochastic programming being optimal in the second step of the problem. Third, we discuss the applicability of simulation-based optimization, combine it with stochastic programming based heuristics and test the resulting new model on a realistic use case. The results are very S Set of stages, s = 1, · · · , |S| B Set of branches in the scenario tree, b = 1, . . . , |B| P Set of paths, ρ = 1, · · · , |P| Input Parameters ∆ r,r Number of time periods to travel from resource r to r . Defined for all pairs (r, r ) ∈ C p q Probability that scenario q occurs f r,r t Fraction of flights from resource r directed to resource r in time period t M r t,q Maximum capacity of PCA r ∈ P in time period t under scenario q ii

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Section: Stochastic Model Comparisons In Terms Of System Delay Costsmentioning

confidence: 73%

mentioning

confidence: 99%

Centralized Disaggregate Stochastic Allocation Models for Collaborative Trajectory Options Program (CTOP)

Zhu

Wei

Hoffman³

et al. 2018

2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC)

Self Cite

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“…However, it does not model the interdependencies within a flight schedule, and cannot capture the characteristics of delay propagation. 7 Another queueing network based model, NASPAC (National Airspace System Performance Analysis Capability), 8 was developed by the MITRE Corporation and adopted by the FAA. It uses a daily flight plan as input and simulates flight times and delays.…”

Section: Air Traffic Simulation Modelsmentioning

confidence: 99%

Time- and space-parallel simulation of air traffic networks

2019

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Computer simulations are widely used to design and evaluate air traffic systems. A fast time simulation capability is essential to effectively explore the consequences of decisions in airspace design, air traffic management, and operations. A parallel simulation approach is proposed to accelerate fast time simulation of air traffic networks that exploits both temporal and spatial parallelisms. A time-parallel algorithm is first described that simulates different time intervals concurrently and uses a fix up computation that exploits the scheduled nature of commercial air traffic to address the problem of dependencies between time segments. The time-parallel algorithm is then extended with a space-parallel simulation approach using Time Warp to simulate each time segment in parallel thereby increasing the amount of parallelism that can be exploited. The time and space-parallel algorithms are evaluated using a simulation of the U.S. National Airspace System (NAS). Experimental data is presented demonstrating that this approach can achieve greater acceleration than what can be achieved by exploiting time-parallel or space-parallel simulation techniques alone.

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“…The FAA's air traffi c controllers, who manage the safe routing of aircraft, delay aircraft on the ground at their origin when they anticipate congestion at the destination; aircraft queuing in the air occurs when there is an unexpected imbalance between capacity and demand, generally due to weather (Moses & Savage, 1990;Vossen, Hoffman, & Mukherjee, 2012). While the link is tenuous, congested environments both in the air and on the ground may be correlated with minor safety faults, such as when the headway between landing aircraft is too short or when two aircraft are present on an active runway at the same time (Endsley & Rodgers, 1997;Majumdar & Ochieng, 2002;Moses & Savage, 1990;Wickens, Mavor, & McGee, 1997).…”

Section: Airport Congestion On the Risementioning

confidence: 99%