TRB brief 1 FAA TFMM Program TFM Research Board (TRB) Meeting Northrup Grumman 16 October 2008

FAA TFMM Program TRB Brief 16 October 2008 Agenda Overview Concept Engineering Infrastructure (Data warehouse) Improved Demand Predictions with Flight Segment Forecasting Improved Takeoff Time Predictions with Surface Movement Data Increased Accuracy of ETAs for Better AARs

FAA TFMM Program TRB Brief 16 October 2008 Analyzing Operational Data to Improve Forecasts These proposals are focused on data-intensive research to show whether new data can improve TFMS accuracy in demand forecasting Proposals address new sources of data and more complex analysis (e.g., looking for airport- and condition-specific rules to improve flight event time predictions) The proposals envision a TFM CE infrastructure that provides access and data management for historical and operational analyses The goal is improving NAS efficiency by providing more dependable forecasts Overview

FAA TFMM Program TRB Brief 16 October 2008 Managing TFM Data for Research Support Much TFM research depends on historical data analysis, but data is distributed and often inaccessible without significant effort Offline data archives are generally structured like the operational data structures that feed them: optimized for transactions but not for analysis and retrieval Data mining of application logs and other inaccessible data stores can provide valuable insight into TFM system and operations behavior A TRB-focused data warehouse optimized for analysis can capture data mined for specific research

FAA TFMM Program TRB Brief 16 October 2008 Correlating Flight Segments for Better Forecasting Currently TFM takes no notice of correlated flight segments (for example, flight 987 from New York to Phoenix via Detroit represents two correlated flight segments) –If 987 is delayed in NY, it will probably be late taking off from Detroit Airframe tail numbers (optionally included in ICAO flight plans [IFPs]) can support coordination of sequential flights using the same equipment –Not always true—airlines can substitute equipment –How often is it true? That’s one of the research questions Treating flights as events coordinated by plans and equipment can potentially improve departure forecasts

FAA TFMM Program TRB Brief 16 October 2008 Using Surface Data to Improve Out-to-Off Estimation Airport operations are predictable if a sufficient number of variables are included in predictions –Examples are found in TRB research and in European CDM Route- and condition-specific out-to-off time estimates can be developed from surface data (e.g., ASDE-X) and airport configuration data Research is needed to identify what data is useful in improving predictability, and to assess the achievable accuracy Goal is an self-correcting tool that periodically updates its forecast model based on measured process times

FAA TFMM Program TRB Brief 16 October 2008 Using TMA Data to Improve TFMS ETAs and AARs Observers have noted that TMA ETAs are more accurate than those provided by TFMS –This can diminish confidence in TFMS demand forecasts –Resulting in overly conservative flight restrictions Research can characterize and quantify the discrepancies between TFMS and TMA under various circumstances If the differences are predictable and consistent, corrective actions (system or operational) may be identified Integration of TFMS and TMA data and forecasts may lead to higher confidence in forecasts and less reliance on defensive constraints

FAA TFMM Program TRB Brief 16 October 2008 Questions? Dave Rhodes/CSC, Director, Advanced ATM Bob Beard/CSC, Chief Engineer, Transportation Solutions Vic Church/CSC, Chief Architect, Transportation Solutions