Relations are estimated among enroute air traffic, controller staffing and performance of controllers and ATC system. Controller staffing is found to increase at least linearly with air traffic in the US National Airspace System. Findings in literature review, FAA controller staffing models, FAA standards…
Contents
Chapter I Introduction
Chapter II Structure of Thesis
Chapter III Literature Review
3.1 Relations between enroute air traffic controller staffing and enroute air traffic in the NAS
3.1.1 US national airspace system and air traffic controller positions staffed for enroute sectors
3.1.2 Function classification of enroute air traffic controller positions
3.1.3 Current method used for controller staffing
3.1.4 Relation between enroute air traffic, controller workload andATC complexity
3.1.5 Measurement of ATC complexity
3.1.6 Relation between air traffic operations and number of controllers staffed in sectors
3.1.7 Factors affecting relation between controller staffing and enrouteair traffic operations
3.1.8 Controller forecasting model developed by FAA for enroute air traffic center controllers
3.1.9 Summary of literature review
3.2 Relations between controller performance and air traffic in sectors and centers of the NAS
3.2.1 Impact of air traffic congestion in sectors and centers
3.2.2 Measures of controller performance and controller workload in sectors and centers
3.2.3 Models developed in literature to relate flight delays/excess distances with congestion in sectors and centers
3.2.4 Difficulties in estimating relations between flight delays/excess distances and air traffic in sectors and centers
3.2.5 Considerations in developing models to estimate relations between flight delays/excess distances and congestion in sectors and centers
3.3 Relations between ATC system performance and enroute air traffic in the NAS
3.3.1 The need to consider entire NAS to estimate relations between delays and enroute traffic volumes by considering monthly and daily measures of delays and enroute traffic volumes in the NAS
3.3.2 Models proposed in literature
3.3.3 Queuing model developed by Wieland 2004 to estimate operational capacity of NAS using OPSNET data
3.3.4 Selection of delay data to measure delays in NAS
3.3.4.1 Delay databases maintained by FAA
3.3.4.2 Drawbacks of data on delay relative to schedule
3.3.5 Suitability of OPSNET database for measuring traffic volumesand delays caused by enroute traffic volumes in the NAS
3.3.5.1 Drawbacks of delay data from OPSNET database
3.3.5.2 Merits of delay and traffic volume data from OPSNET database
3.4 Overview of Methodology
3.4.1 Relation between controller staffing and enroute air traffic in NAS
3.4.2 Relations between controller performance and air traffic in sectors and centers of NAS
3.4.3 Relations between ATC system performance and enroute traffic volumes in the NAS
Chapter IV Relations between Enroute Air Traffic Controller Staffing and Enroute Air Traffic in the NAS
4.1 Proposed analysis
4.1.1 Relation between ATC complexity for centers and air traffic operations in centers
4.1.2 Relation between air traffic operations and distribution of air
traffic operations in centers during the peak 1830 hours and the second busiest 1830 hours of a 365 day period
4.1.3 Relation between monthly onboard controller staffing in centers and monthly center operations
4.1.4 Validation of current controller forecasting model by comparing model predicted monthly staffing and actual on board monthly staffing of controllers
4.1.5 Relation between number of dynamic sectors in a center and that center’s air traffic operations
4.2 Analyses and results
Chapter V Relations between Controller Performance and Air Traffic in Sectors and Centers of the NAS
5.1. Measures of air traffic activity (controller workload) in sectors and centers
5.2. Measures of controller performance in sectors and centers
5.3. Data used for analyses
5.4. Proposed models
5.4.1. Relations between controller performance and controller workload for the same center/sector
5.4.2. Effect of congestion in successive center/sector on flight path
5.4.3. Analysis of flights traveling between a city pair
5.4.4. Performance comparison of R and R & D controller staffing configurations in a sector
5.5. Selection of centers and sectors and details of data used
5.6. Data processing tools and statistical softwares used for performing analyses
5.7. Analyses and results
5.8. Comparison of results of models estimated in section 5.7 with results of Howell et. al. (2003)
Chapter VI Relations between ATC System Performance and Enroute Air Traffic in the NAS
6.1 Organization of chapter
6.2. Difficulties in estimating relations between ATC system performance and enroute traffic volume in the NAS
6.3. Analyses proposed to estimate relations between recorded flight delays and enroute traffic volume in the NAS
6.4. Proposed extension to Wieland`s model
6.5. Proposed analyses
6.5.1. Analysis performed using delays specifically caused by enroute congestion which are recorded by OPSNET database as delays by cause “center volume”
6.5.2. Analyses performed using different forms of delays used to reduce air delays caused by enroute congestion
6.5.2.1. Analysis of average ground delay and number of operations delayed by category-arrival, departure and
enroute
6.5.2.2. Analysis of average minutes of delay by category- airport departure delay, gate departure delay, taxi-in/out delay, airborne delay, block delay and gate arrival delay
6.5.3. Trends in the variation of different delay types
6.6. Analyses and results
6.7. Results
6.8. Interpretation of results
6.8.1 Interpretation of results from section
6.7
6.8.2 Interpretation of results from section
6.8.3 Relation between delays and enroute traffic volume in the NAS
6.8.4. Reasons for low explanatory power of the monthly and month-specific models
6.9. Drawbacks of analyses
6.9.1. Drawbacks of data used in the analyses
6.9.2. Drawbacks of month-specific models
Chapter VII Conclusions
7.1 Relations between enroute air traffic controller staffing and enroute traffic in the NAS
7.2 Relations between controller performance and air traffic in sectors and centers of NAS
7.3 Relations between ATC system performance and enroute air traffic in the NAS
7.4 Models and results which can be incorporated in the FAA NAS Strategy Simulator
Chapter VIII Recommendations for Future Work
8.1. Relation between delays due to understaffing of controllers and controller staffing/enroute traffic in the NAS
8.2. Analyses using variable “controller work minutes in a center” 296
8.3. Models estimated using minutes of delays due to enroute congestion
8.4. Revision of Position Classification Standard for ATC (FAA 1999), currently used by FAA to measure center complexities and assign controller grades & wages
8.5. Revision and validation of FAA 1997 standards
8.6. Analyses to be performed after obtaining the required data
8.6.1. Analysis 4.1.5 – Relation between number of dynamic sectors in a center and air traffic operations handled by that center
8.6.2. Analysis 9.3.1 – Sector MAP values are used to measure NAS performance, for estimating relations between NAS performance and enroute traffic volumes
8.6.3. Analysis 9.3.2. Enroute delays caused by Traffic Management processes are used as measures of NAS performance, for estimating relations between NAS performance and enroute traffic volumes
8.7. Estimating three-dimensional relations among NAS enroute
traffic demand, controller staffing and NAS performance
Chapter IX Unrealized Analyses
9.1. Analysis proposed to estimate relations between flight times and
enroute traffic volumes in the NAS
9.2. Analysis proposed to estimate relations between excess distances
traveled by flights and enroute traffic volume in the NAS
9.3. Analyses proposed to estimate relations between NAS
performance measures and NAS enroute traffic volumes
9.3.1. Sector MAP value is used as a NAS performance measure for
estimating relations between NAS performance and enroute
traffic volume
9.3.2. Enroute delays caused by Traffic Management processes are
used as measures of NAS performance, for estimating relations x
between NAS performance and enroute traffic volume
References
Author: Kamble, Sameer Datta
Source: University of Maryland
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