The Miles-in-Trail Impact Assessment Capability

Ostwald, Paul; Topiwala, Tejal; DeArmon, James

doi:10.2514/6.2006-7780

Cited by 12 publications

(8 citation statements)

References 2 publications

(1 reference statement)

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“…(3) //set the first T imb − 1 of array to 1, which corresponds to a solution; (4) for i ⟵ 0 to T imb − 1 do (5) index[i] � 1; (6) end for (7) Store the solution according to the values of array index[ ]; (8) while has Done (index, N, T imb ) do (9) for i ⟵ 0 to N + 1 do (10) //find the first 1-0 combination and change it to 0-1 combination; (11) if index[i] � � 1 and index[i] � � 0 then (12) index[i] � 0; (13) index[i] � 1; (14) Store the solution from index[ ]; (15) //move 1 of the left of 0-1 combination to the left of array; (16) int count � 0; (17) for j ⟵ 0 to i do (18) if index[j] � � 1 then (19) index[j] � 0; (20) index[count++] � 1; (21) end if (22) end for (23) end if (24) break; (25) end for (26) end while ALGORITHM 2: 0-1 combination algorithm for COR i . where Cost g and L g are the delay cost and air traffic control load if corridor COR g uses the strategy that node u represents.…”

Section: Strategy Generation Algorithm For Each Corridor Cormentioning

confidence: 99%

“…In [17], the authors presented a perspective on the MIT strategy, where the strengths and shortcomings of MIT were discussed in details and proved that MIT is an effective traffic management strategy for high-density sectors. Ostwald et al [18] proposed an operational concept for arrival MIT restrictions using the MIA capability, where MIA is the tool used to evaluate the impacts of the proposed MIT restrictions on resources and flights before implementing them. Sheth et al [19,20] developed a model to compute MIT and pass back restrictions in the NAS for current traffic conditions, and the maximum ground delay and absorbable airborne delay are incorporated in the model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generalized Method of Modeling Minute‐in‐Trail Strategy for Air Traffic Flow Management

Huang

Yan

et al. 2019

Mathematical Problems in Engineering

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With the rapidly increasing air traffic demand, the demand-capacity imbalance problem of sector is surfaced gradually. And, minute-in-trail/miles-in-trail (MIT) is an effective strategy to balance the traffic demands and capacity. In this work, we consider the MIT strategy generation problem for the situation that a sector with NC corridors is affected by convection weather for Timb time periods. Given the sector capacity Cwt, t=1,…,Timb, under convection weather, we propose a three-phase optimization framework to generate E-MIT strategy to achieve the demand-capacity balance. First, we take the sector capacity of Timb time periods under convection weather as a whole, that is, ∑t=1TimbCwt, and then a dynamical programming-based method is proposed to allocate ∑t=1TimbCwt for NC corridors such that the capacity resources Awi of each corridor CORi, i=1,…,NC, can be determined. Second, a 0-1 combination algorithm is used to allocate the capacity resources Awi into Timb time periods for each corridor CORi such that the candidate strategies set CSi of each corridor can be determined, where a strategy solji∈CSi is an array with Timb numbers and each number represents the maximum allowed number of flights entering into sector from CORi in one time period. Finally, a modified shortest path algorithm based on the backtracking method is taken to select the optimal strategy from CSi for NC corridors such that the total delay cost and air traffic control load are minimized. Additionally, a dynamical programming-based method is proposed to generate E-MIT strategy for the special case that the sector capacities of different time periods under convection weather are the same, that is, Cw1=Cw2=⋯=CwTimb, and the generated strategies of Timb time periods for a corridor are also the same. Experimental results show that compared with the proposed three-phase optimization method, rate-based method and need-based method will spend more 8.1% and 6.3% of delay cost, respectively. When considering the special case, the experimental results show that compared with the proposed dynamical programming-based method, the rate-based method and need-based method will spend more 10.2% and 7.5% of delay cost, respectively.

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Section: Strategy Generation Algorithm For Each Corridor Cormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generalized Method of Modeling Minute‐in‐Trail Strategy for Air Traffic Flow Management

Huang

Yan

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…A refined modeling of the initial work presented by Wanke et al was presented in two subsequent works. They are by Ostwald [6] on the MIT impact assessment capability, and by DeArmon [7] on the validation of the MIT model. The reported MIT model appears to be a robust capability but some of the implementation issues (e.g., the range limit, multiple stream analysis, passback of restrictions, etc.)…”

Section: Introductionmentioning

confidence: 99%

Development of miles-in-trail passback restrictions for air traffic management

Sheth

Gutierrez-Nolasco

2014

2014 IEEE/AIAA 33rd Digital Avionics Systems Conference (DASC)

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This paper presents modeling of miles-in-trail passback restrictions for use in air traffic management. Generally, FAA managers employ miles-in-trail as a traffic management initiative when downstream traffic congestion at airports or in sectors is anticipated. In order to successfully implement the miles-in-trail at airspace fixes or navigational aids, it is desired that restriction values be computed for passing back to upstream facilities at specific boundaries. This paper presents a model which can be used for that purpose. This model improves on a previous version using traffic manager feedback resulting in significant improvement in guidance. The modeling approach is described along with lessons learned and improvements made during model development. Results for two sample traffic and one real traffic scenarios are presented. Additional operational considerations required by the traffic managers to implement the passback restrictions, namely maximum ground delay and absorbable airborne delay are incorporated in the model. A main result of this research is that absorbing small amount of ground and airborne delays are sufficient to handle the imposed constraint. Another finding is that implementing the passback restrictions provides the traffic managers ways to alleviate traffic constraints to help reduce excessive airborne delay for current traffic conditions.

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“…A refined modeling of the initial work presented in Wanke, et al was presented in two subsequent works. They are by Ostwald 6 on the MIT impact assessment capability and by DeArmon 7 on the validation of the MIT model. The reported MIT modeling appears to be a robust capability but some of the implementation parameters (e.g., the range limit, multiple stream analysis, passback of restrictions, etc.)…”

Section: Introductionmentioning

confidence: 99%

Analysis and Modeling of Miles-in-Trail Restrictions in the National Airspace System

Sheth

Gutierrez-Nolasco

Petersen

2013

2013 Aviation Technology, Integration, and Operations Conference

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This paper presents an analysis of values and locations of Miles-in-Trail restrictions used within the National Airspace System over the last three years. Using specific severe weather avoidance routes, various locations are selected to implement the Miles-in-Trail restrictions to study their individual impact on the delay of flights and sector congestion in the airspace. The current traffic management operational infrastructure lacks the modeling of multiple restrictions with passback Miles-in-Trail values to upstream facilities. The model developed here allows implementation of multiple restriction locations for multiple merging streams of traffic. The model also permits speed control, vectoring or airborne holding, and passback of restrictions to upstream facilities. The simulation environment allows implementation of these restrictions, enabling a what-if capability in a rapid evaluation mode for Miles-in-Trail impact. Preliminary results are presented for delay of impacted flights due to implementation of three different playbook routes and Miles-in-Trail values at various locations with passbacks to upstream facilities. Results of sector congestion in the airspace for those cases are discussed as well. It was observed that for a particular playbook route implementation with Miles-in-Trail between 25 and 30 nmi applied at a reference fix resulted in low total delay and sector congestion. Overall, the model appears to be a good starting point for evaluation of passback restriction impact and, with operational feedback, could be used for advising passback values to upstream facilities.

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The Miles-in-Trail Impact Assessment Capability

Cited by 12 publications

References 2 publications

Generalized Method of Modeling Minute‐in‐Trail Strategy for Air Traffic Flow Management

Generalized Method of Modeling Minute‐in‐Trail Strategy for Air Traffic Flow Management

Development of miles-in-trail passback restrictions for air traffic management

Analysis and Modeling of Miles-in-Trail Restrictions in the National Airspace System

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