Survey of Methods to Predict Controller Workload for Real-Time Monitoring of Airspace Safety

The Real Time Safety Monitoring (RTSM) approach allows for assessment and prediction of the safety margin in the National Airspace System (NAS) to help preempt incidents and accidents, rather than having to reactively mitigate them. In RTSM, the NAS is modeled using state variables, and safety metrics are defined in terms of these state variables. The safety metrics have been classified as weather-related, airspace-related, and human-related. Many of the formulated human-related safety metrics need an estimate of the controller workload for their computation. However, this computation is not trivial. Hence, in this report, we perform a literature survey to identify the different factors that enable the computation of controller workload and categorize these factors. Next, we describe studies undertaken to determine a minimal set of factors that provide a correct assessment of controller workload. Lastly, we survey approaches for evaluating how well the selected factors correlate with the controllers’ subjective assessment of their workload. Based on this survey, we present factors beneficial to computing and predicting controller workload in real time, and discuss the status of data sources necessary for these computations.

[1]  David K. Schmidt.  QUEUEING ANALYSIS OF THE AIR TRAFFIC CONTROLLER'S WORK LOAD , 1978 .

[2]  A J Hudgell,et al.  Assessing the Capacity of Novel ATM Systems , 2001 .

[3]  Brian Marr,et al.  Controller Workload-based Calculation of Monitor Alert Parameters for En Route Sectors , 2015 .

[4]  G. Lawton,et al.  Pilot perceptions of airspace complexity. Part 2 , 2004, The 23rd Digital Avionics Systems Conference (IEEE Cat. No.04CH37576).

[5]  Parimal Kopardekar,et al.  Analysis of Current Sectors Based on Traffic and Geometry , 2008 .

[6]  Banavar Sridhar,et al.  Impact of Uncertainty on the Prediction of Airspace Complexity of Congested Sectors , 2011 .

[7]  Matthew Daigle,et al.  Real-Time Monitoring and Prediction of Airspace Safety , 2018 .

[8]  G. B. Chatterji,et al.  Neural network based air traffic controller workload prediction , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[9]  Amy R. Pritchett,et al.  Describing Airspace Complexity: Airspace Response to Disturbances , 2009 .

[10]  Banavar Sridhar,et al.  Airspace Complexity and its Application in Air Traffic Management , 1998 .

[11]  George L. Donohue,et al.  Temporal and Spatial Distribution of Airspace Complexity for Air Traffic Controller Workload-Based Sectorization , 2004 .

[12]  Chetan S. Kulkarni,et al.  Predicting Real-Time Safety of the National Airspace System , 2016 .

[13]  Parimal Kopardekar,et al.  Airspace Complexity Measurement: An Air Traffic Control Simulation Analysis , 2009 .

[14]  Dengfeng Sun,et al.  Workload evaluation of sectorized air traffic control and stream management , 2012, 2012 Integrated Communications, Navigation and Surveillance Conference.

[15]  Paul Milgram,et al.  Combining Time and Intensity Effects in Assessing Operator Information-Processing Load , 1997, Hum. Factors.

[16]  Arnab Majumdar,et al.  Factors Affecting Air Traffic Controller Workload: Multivariate Analysis Based on Simulation Modeling of Controller Workload , 2002 .

[17]  Eric Feron,et al.  Trajectory Clustering and an Application to Airspace Monitoring , 2011, IEEE Trans. Intell. Transp. Syst..

[18]  Anthony J. Masalonis,et al.  DYNAMIC DENSITY AND COMPLEXITY METRICS FOR REALTIME TRAFFIC FLOW MANAGEMENT , 2003 .

[19]  Matthew Daigle,et al.  Real Time Safety Monitoring: Concept for Supporting Safe Flight Operations , 2017 .

[20]  S. Puechmorel,et al.  AIR TRAFFIC COMPLEXITY : TOWARDS INTRINSIC METRICS , 2000 .

[21]  Carol A. Manning,et al.  USING AIR TRAFFIC CONTROL TASKLOAD MEASURES AND COMMUNICATION EVENTS TO PREDICT SUBJECTIVE WORKLOAD , 2002 .

[22]  C. Collet,et al.  Complexity and Controller Workload : Trying to Bridge the Gap , 2002 .

[23]  J. Post,et al.  (EN-012) Validation of En Route Capacity Model with Peak Counts from the US National Airspace System , 2010 .

[24]  Peter Brooker,et al.  Controller workload, airspace capacity and future systems. , 2003 .