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Performance Requirements – Dynamic Density & Dynamic Resectorization Concepts Rich Jehlen Manager, Air Traffic Planning Division Toulouse June, 2002.

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Presentation on theme: "Performance Requirements – Dynamic Density & Dynamic Resectorization Concepts Rich Jehlen Manager, Air Traffic Planning Division Toulouse June, 2002."— Presentation transcript:

1 Performance Requirements – Dynamic Density & Dynamic Resectorization Concepts Rich Jehlen Manager, Air Traffic Planning Division Toulouse June, 2002

2 2 Background The ATM system remains, at its core, a human-based enterprise As a result, performance requirements and metrics of the various concepts are measures of human behavior, response or performance The increasing use of Decision Support Tools (DSTs) to increase system efficiency and maximize use of capacity require an even greater investment in human factors analysis of the aggregate effect on human capabilities

3 3 Dynamic Density – Framework Current method of sector workload relies on counting the number of aircraft in a sector for a specified period –Strategy does not account for human task load associated with airspace complexity, aircraft interaction (conflicts), etc Inaccuracies resulting from prediction errors and lack of human context for sector environment make present tools unable to support the advanced operations of Free Flight and Dynamic Resectorization Dynamic Density is intended as an objective measure of sector complexity that takes into account both the number of aircraft and the environment (airspace, convergence angles, etc) –To be used to project needed operational responses (e.g. sector adjustments, reduction of Free Flight activity is required, additional controllers are needed) FL370 FL350 FL330 FL310 Low Traffic Complexity FL245 370 FL350 FL330 240 FL290 TOC TOD Higher Traffic Complexity

4 4 Dynamic Density – Analysis/Status Several research initiatives resulted in differing algorithms using different parameters and inputs Research organizations brought together to identify data elements necessary to facilitate a single data gathering activity –Data included interviews and assessments from controllers and supervisors. These were used to determine true density levels –Data collected from multiple ARTCCs(ACCs) with standardized format and methodology –Portion of data (~2/3) provided to researchers to finalize proposed algorithms & accomplish internal tests of accuracy –Final portion of data (~1/3) to be used for independent test/analysis of performance of each initiative – and compare to current Monitor Alert (based on sector count) –Analysis planned to include performance comparison based on airspace characteristics (mostly cruise phase, climbing/descending, etc) Success based on level of conformance to supervisor/controller assessments Highest rated algorithm under differing airspace characteristics may be combined for best overall performance and inclusion in operational system Testing & analysis due for completion next month. Report output available upon request.

5 5 Ability to align airspace structures with prevailing air traffic flow patterns in real-time Changes in airspace configuration to reflect current traffic operations DR Dynamic Resectorization - Framework Sector Boundary Traffic Flows

6 6 Research needed to determine the acceptable range of adjustment intervals: Dynamic Resectorization – Analysis Traffic Predictability (T + n) Situational Awareness Airspace Changes & Workload Ability to accept additional taskload Acceptable Ranges for Human Performance How much airspace can be moved? [How many miles from the previous boundary?] How often can the airspace be moved without adversely impacting controller situational awareness? How variable can the airspace be? [Infinitely, set increments such as 5 or 10 miles] What level of traffic predictability/stability is necessary to meet timeliness consideration? [Dynamic Density] How fast (responsive) must the system be? At what level is the controller unable to deal with the tasks necessary to move the airspace (to reduce the workload)? T0T0

7 7 Weather Impact Traffic Index (WITI) Intent is to develop a method that allows system-wide performance (e.g., flight time, delay, etc.) in different time periods (i.e., day, week, month, year) to be compared to each other while accounting for differences caused by variations in the weather –Weather impacts account for approximately 70% of the delays experienced –Time periods such as Summer 2000 have different weather than Summer 2001 –How can performance of one time period with good weather be compared to another with bad weather? Since weather has such a large influence, it cannot be ignored when measuring performance The output of the measure will reflect system response to anomalous weather events in terms of performance metrics that reflect the quality of ATM services delivered. The actual output is based on human responses (specific course of actions selected) and behaviors (execution of the selected actions – both on the ground and flight deck). This is true regardless of the level of automation support provided

8 8 Weather Impacting Traffic Index (WITI) - Framework Weighting Factors Grid (50 nmi grid cells) Note location of high weight values (larger numbers) are in the east, central, and coastal areas and lower values are in the upper mid- west and extreme south- west

9 9 Summary Man is the measure of all things, of things that are that they are, and of things that are not that they are not. –Protagoras (c. 481–411 B.C.) The establishment of performance requirements and performance metrics must be viewed from the human perspective. Automation in the future operational concepts may have the unintended effect of obscuring the underlying human- based system dependencies

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