Using Knowledge Exploration Tools to Study Airspace Complexity in Air Traffic Control

Mogford, Richard H.; Murphy, Elizabeth; Guttman, Jeremy A.

doi:10.1207/s15327108ijap0401_2

Cited by 23 publications

(20 citation statements)

References 4 publications

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“…measuring complexity, has risen. Mogford et al [6,7] were some of the first to deal with complexity and its influence on air traffic controllers (ATCo) workload. They identified two basic elements of ATC complexity: sector complexity and traffic complexity.…”

Section: Complexity Of Air Trafficmentioning

confidence: 99%

Relationship between Air Traffic Demand, Safety and Complexity in High-Density Airspace in Europe

Pejovic¹,

Netjasov²,

Crnogorac³

2020

Risk Assessment in Air Traffic Management

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Air traffic performance of the European air traffic system depends not only on traffic demand but also on airspace structure and its traffic distribution. These structural (airspace structure) and flow characteristics (factors such as traffic volume, climbing/descending traffic, mix of aircraft type, military area activity) influence airspace complexity, which can affect controller workload and influence the probability of safety occurrence. In other words, all these dynamic and static complexity components can potentially have an impact upon the safety of the air traffic management (ATM) system. Having in mind fluctuation in traffic on daily, seasonal or annual level in certain airspace, a few questions arise: How changes in traffic demand influence complexity and conflict risk? Is there any correlation between traffic demand, conflict risk and complexity? and Are there any differences between seasons? For that purpose, an investigation is performed on FAB Europe Central (FABEC) airspace, based on 2 weeks of operated traffic during the summer and fall of 2017. Air traffic complexity is estimated using the EUROCONTROL complexity metrics, while conflict risk is assessed using the conflict risk assessment simulation tool. Results show that certain positive relationship exists between traffic demand, conflict risk and complexity.

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Section: Complexity Of Air Trafficmentioning

confidence: 99%

Relationship between Air Traffic Demand, Safety and Complexity in High-Density Airspace in Europe

Pejovic¹,

Netjasov²,

Crnogorac³

2020

Risk Assessment in Air Traffic Management

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“…Several studies have explored sector activity and taskload in various ways using simulation studies (Buckley, DeBaryshe, Hitchner, & Kohn, 1983;Stein, 1985;Mogford, Murphy, and Guttman, 1994;Pawlak, Brinton, Crouch, and Lancaster, 1996). The methodology most often used assumes that many variables affecting activity in an airspace also influence the perceived workload and the objective taskload of the controller.…”

Section: History Of Workload Taskload and Complexity Measurementmentioning

confidence: 99%

“…In a separate study, Stein (1985) exposed controllers to different levels of airspace activity and concluded that three variables (i.e., Aircraft Count, Clustering, and Restricted Airspace) significantly influenced mental workload. More recently, Mogford, Murphy, and Guttman (1994) used verbal reports from air traffic control specialists and multidimensional scaling to identify a list of 16 factors that contribute to airspace complexity. Finally, Pawlak, Brinton, Crouch, and Lancaster (1996) focused on controllers' strategies and decision-making activities and proposed a list of 15 factors that may influence perceived air traffic complexity.…”

Section: History Of Workload Taskload and Complexity Measurementmentioning

confidence: 99%

“…However, the average number of aircraft under the sector's control does not convey any information about the proximity of the aircraft. If traffic characteristics are a contributor to workload (as proposed by Mogford et al, 1994 andPawlak et al, 1996) then it might also be advantageous to include one or more proximity measures in the POWER suite. The information necessary to compute such measures is readily available and so the addition is feasible.…”

Section: Component 3 -Objective Workloadmentioning

confidence: 99%

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POWER: Objective activity and taskload assessment in en route air traffic control.

Mills¹,

Pfleiderer²,

Manning³

2002

PsycEXTRA Dataset

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Two computer programs, the National Airspace System (NAS) Data Management System (NDMS) and the Performance and Objective Workload Evaluation Research (POWER) program, have been developed to provide a platform for quantifying en route air traffic controller activity and taskload. The NDMS program extracts data produced by en route mainframe computers and encodes the information into database files that provide efficient storage and access. The POWER program calculates specific measures using aircraft positions and controller data entries. The development and use of such measures is important for establishing baseline activity measures and for evaluating modifications to ATC systems. NAS System Analysis Recording (SAR) data were collected from the Jacksonville en route air traffic control center between 8:30-10:30 a.m. and between 12:00-2:00 p.m. (local time) for each of four consecutive days. POWER measures were computed in 30-minute intervals for all active sectors. A Principal Components Analysis (PCA) was conducted to evaluate the current set of POWER variables and provide guidelines for the addition of new measures or the modification of existing ones. PCA with Varimax rotation converged in seven iterations and produced five components with eigenvalues > 1. Cumulatively, the four components accounted for 68.18% of the variability in the data set: Component 1 (Activity) accounted for 26%, Component 2 (Flight Path Variability) accounted for nearly 13%, Component 3 (Objective Workload) accounted for 11%, Component 4 (D-side Activity) accounted for 9%, and Component 5 (Overload) accounted for approximately 8%. Variables comprising the five extracted components provided valuable information about the underlying dimensions of the NAS data set. Additions or modifications that might improve the ability of POWER to describe ATC activity and taskload were identified.17.

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“…Aircraft mix has often been proposed as one of the traffic characteristics contributing to sector complexity in en route air traffic control (Robertson, Grossberg, & Richards, 1979;FAA, 1984;Grossberg, 1989;Mogford, Murphy, & Guttman, 1994) . Although the Aircraft Mix Index (Pfleiderer, 2003) failed to contribute significantly to the explained ariance in a linear regression analysis of subjecti e complexity ratings (Pfleiderer, 2005), it may pro e to be a useful predictor of OEs .…”

Section: Prediction and Classification Of Operational Errors And Routmentioning

confidence: 99%

Prediction and Classification of Operational Errors and Routine Operations Using Sector Characteristics Variables

Pfleiderer¹,

Manning²

1995

PsycEXTRA Dataset

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This study examined prediction and classification of operational errors (OEs) and routine operations (ROs) using sector characteristics variables. Average Control Duration, Aircraft Mix Index, Average Lateral Distance, Average Vertical Distance, Number of Handoffs, Number of Point Outs, Number of Transitioning Aircraft, and Number of Heading Changes were used as predictors in two stepwise logistic regression analyses conducted for the high-altitude and low-altitude sectors. In the high-altitude sample, variables included in the final model (Number of Heading Changes, Number of Transitioning Aircraft, and Average Control Duration) accurately classified OE and RO samples for 80% of the cases. In the low-altitude sample, variables included in the final model (Number of Point Outs, the Number of Handoffs, and the Number of Heading Changes) accurately classified OE and RO samples for 79% of the cases. Although logistic regression cannot be used to determine causation, it effectively identified variables that predicted the occurrence of OEs.17.

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Using Knowledge Exploration Tools to Study Airspace Complexity in Air Traffic Control

Cited by 23 publications

References 4 publications

Relationship between Air Traffic Demand, Safety and Complexity in High-Density Airspace in Europe

Relationship between Air Traffic Demand, Safety and Complexity in High-Density Airspace in Europe

POWER: Objective activity and taskload assessment in en route air traffic control.

Prediction and Classification of Operational Errors and Routine Operations Using Sector Characteristics Variables

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