Validation of the Miles-in-Trail Modeling Function in a Traffic Flow Management Concept Prototype

DeArmon, James; Wan, Yan; Berry, Debra L.

doi:10.2514/6.2011-6861

Cited by 5 publications

(3 citation statements)

References 1 publication

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“…A similar interpretation can be made for TBFM departure scheduling capability, MIT restrictions, and TBFM EDC capability. This is in line with the findings of a previous study that demonstrated that as the amount of MIT restrictions increase and parameters become more aggressive, the size of departure delays increases as well ( 57 ). The results of the current study provide additional insights into the efficiency of TMIs and their impacts on the departure delay during various operational scenarios.…”

Section: Discussionsupporting

confidence: 92%

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

Esmaeilzadeh

Mokhtarimousavi

2020

Transportation Research Record: Journal of the Transportation R

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The expected growth in air travel demand and the positive correlation with the economic factors highlight the significant contribution of the aviation community to the U.S. economy. On‐time operations play a key role in airline performance and passenger satisfaction. Thus, an accurate investigation of the variables that cause delays is of major importance. The application of machine learning techniques in data mining has seen explosive growth in recent years and has garnered interest from a broadening variety of research domains including aviation. This study employed a support vector machine (SVM) model to explore the non-linear relationship between flight delay outcomes. Individual flight data were gathered from 20 days in 2018 to investigate causes and patterns of air traffic delay at three major New York City airports. Considering the black box characteristic of the SVM, a sensitivity analysis was performed to assess the relationship between dependent and explanatory variables. The impacts of various explanatory variables are examined in relation to delay, weather information, airport ground operation, demand-capacity, and flow management characteristics. The variable impact analysis reveals that factors such as pushback delay, taxi-out delay, ground delay program, and demand-capacity imbalance with the probabilities of 0.506, 0.478, 0.339, and 0.338, respectively, are significantly associated with flight departure delay. These findings provide insight for better understanding of the causes of departure delays and the impacts of various explanatory factors on flight delay patterns.

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Section: Discussionsupporting

confidence: 92%

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

Esmaeilzadeh

Mokhtarimousavi

2020

Transportation Research Record: Journal of the Transportation R

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“…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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Validation of the Miles-in-Trail Modeling Function in a Traffic Flow Management Concept Prototype

Cited by 5 publications

References 1 publication

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

Machine Learning Approach for Flight Departure Delay Prediction and Analysis

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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