Trajectory Option Set Planning Optimization under Uncertainty in CTOP

Cruciol, Leonardo L. B. V.; Clarke, John‐Paul; Weigang, Li

doi:10.1109/itsc.2015.337

Cited by 15 publications

(7 citation statements)

References 3 publications

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“…While it can be clearly demonstrated that submitting alternative route options improves system efficiency [23][24][25], there has been hesitancy to do so in practice, possibly because of equity concerns. Indeed, some studies [10,35,37,38] show that gaming may be an issue in CTOP. Thus, it is critical to consider equity within allocation.…”

Section: B Efficiency and Equity Trade-off In Tmismentioning

confidence: 99%

Efficient trajectory options allocation for the collaborative trajectory options program

Rodionova

Arneson

Sridhar

et al. 2017

2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)

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The Collaborative Trajectory Options Program (CTOP) is a Traffic Management Initiative (TMI) intended to control the air traffic flow rates at multiple specified Flow Constrained Areas (FCAs), where demand exceeds capacity. CTOP allows flight operators to submit the desired Trajectory Options Set (TOS) for each affected flight with associated Relative Trajectory Cost (RTC) for each option. CTOP then creates a feasible schedule that complies with capacity constraints by assigning affected flights with routes and departure delays in such a way as to minimize the total cost while maintaining equity across flight operators. The current version of CTOP implements a Ration-by-Schedule (RBS) scheme, which assigns the best available options to flights based on a First-Scheduled-First-Served heuristic. In the present study, an alternative flight scheduling approach is developed based on linear optimization. Results suggest that such an approach can significantly reduce flight delays, in the deterministic case, while maintaining equity as defined using a Max-Min fairness scheme.

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Section: B Efficiency and Equity Trade-off In Tmismentioning

confidence: 99%

Efficient trajectory options allocation for the collaborative trajectory options program

Rodionova

Arneson

Sridhar

et al. 2017

2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC)

View full text Add to dashboard Cite

show abstract

“…As the latest technical extension in the field of CDM, CTOP is oriented to optional en-route resources and time slot resources, which mainly involves two aspects: airlines submitting trajectory options set (TOS, it includes trajectory parameters such as route, altitude, speed, and cost of per trajectory) to express their trajectory preferences and ATCSCC finally selecting the en-route resource allocation scheme [12]. Therefore, some studies have integrated game theory with global deployment strategies of en-route resources dominated by ATCSCC, such as slot allocation and rerouting: applying cooperative game theory to CTOP slot allocation strategies, establishing single-stage delay allocation model [13], slot switching model [14], and cooperative slot allocation model [15] among multiple airlines to provide airlines with better time slot resource allocation schemes and to reduce its delay and increase their participation in the slot allocation process. The game equilibrium theory is applied to the CTOP rerouting strategy, which mainly takes the principle of efficiency of resource allocation and the principle of optimal interests of airlines as the conflict points of game participants: establishing the optimal trajectory selection model [16], the global information exchange model [17], rerouting trajectory allocation model [18], and so on.…”

Section: Introductionmentioning

confidence: 99%

The Method of Trajectory Selection Based on Bayesian Game Model

et al. 2022

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To cope with the problem that most of the en-route spatial-temporal resource allocation in the collaborative trajectory options program (CTOP) only considers the air traffic control system command center (ATCSCC) while ignoring the needs of the airlines, which results in the loss of fairness, this study explores resource allocation methods oriented to airline trajectory preferences with optional trajectory and entry slots of flights over the flow constrained area (FCA) as the research object. Using game theory to analyze airline trajectory preference information and a Bayesian game model based on mixed strategies is constructed, the process of incomplete information game among airlines is studied. The equilibrium theory is used to solve the guarantee strategy of airline trajectory selection, which makes the airline trajectory selection strategy robust and provides a basis for the selection of schemes for ATCSCC to implement en-route network resource allocation under the CTOP. Experimental analysis was carried out to verify the feasibility of the method based on the actual operation data of high-altitude sectors of Shanghai. The results show that the solution obtained by the game can provide airlines with flight trajectory and entry slots over the FCA that are more in line with their actual operational needs and which provide data reference for the ATCSCC to select the final plan in multiple global Pareto optimal solutions in the subsequent process of the CTOP so as to better play the decision-making role of airlines in the CTOP while improving the fairness of en-route resource allocation.

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“…There are many competitive settings in which players have asymmetric information about the underlying state of the game. Examples include cyber security problems [1], [2], resource competitions in air transportation systems [3], [4], national defense [5], [6], economic systems [7], power networks [8] and so on. In these systems, because of the noncooperation between players, a player usually holds private information that is not shared with the other players, which causes the information asymmetry in games.…”

Section: Introductionmentioning

confidence: 99%

Efficient Strategy Computation in Zero-Sum Asymmetric Information Repeated Games

Shamma

2020

IEEE Trans. Automat. Contr.

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Zero-sum asymmetric games model decision making scenarios involving two competing players who have different information about the game being played. A particular case is that of nested information, where one (informed) player has superior information over the other (uninformed) player. This paper considers the case of nested information in repeated zerosum games and studies the computation of strategies for both the informed and uninformed players for finite-horizon and discounted infinite-horizon nested information games. For finitehorizon settings, we exploit that for both players, the security strategy, and also the opponent's corresponding best response depend only on the informed player's history of actions. Using this property, we refine the sequence form, and formulate an LP computation of player strategies that is linear in the size of the uninformed player's action set. For the infinite-horizon discounted game, we construct LP formulations to compute the approximated security strategies for both players, and provide a bound on the performance difference between the approximated security strategies and the security strategies. Finally, we illustrate the results on a network interdiction game between an informed system administrator and uniformed intruder.

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Trajectory Option Set Planning Optimization under Uncertainty in CTOP

Cited by 15 publications

References 3 publications

Efficient trajectory options allocation for the collaborative trajectory options program

Efficient trajectory options allocation for the collaborative trajectory options program

The Method of Trajectory Selection Based on Bayesian Game Model

Efficient Strategy Computation in Zero-Sum Asymmetric Information Repeated Games

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