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1 NEXTOR Monitoring and Modeling NAS Performance at the Daily Level Mark Hansen Performance Metrics TIM May 2002

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2 NEXTOR Normalization Translate before/after performance comparisons to with/without comparisons Enable effects Beyond FAA Control to be removed while examining metrics of direct economic relevance Enable comparison of observed and predicted (by models) impacts of technology deployments

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3 NEXTOR NEXTOR Normalization Effects of FFP1 on Terminal Area and En Route Performance Focus on Delays and Time-in-System Metrics Presented Here Effect of TTMA at LAX Effect of URET

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4 NEXTOR Modeling Cycle Models R&D and Deployment Decisions Introduction of New Systems Benefits and Impacts of New Systems Normalization

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5 NEXTOR Conceptual Framework Demand Weather Conditions at other Airports Time at Origin Airborne Time Taxi-In Time Total Flight Time System Performance En Route Terminal Area ATM Aircraft

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6 NEXTOR Daily Flight Time Index (DFTI) Daily weighted average of flight times to a given airport from a set of origins Flight time= Actual Arrival Time - Scheduled Departure Time Scheduled Flight Time+Departure Delay+Flight Time Delay Time-at-Origin+Airborne Time+Taxi-In Time Origins have at least one completed flight in each day of sample Weights reflect origin share of flights to study airport over study period

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9 Weather Normalization Based on CODAS hourly weather observations for LAX Factor analysis of weather data Create small number of factors that capture variation in large number of variables Factors are linear combinations of original variables Factors correspond to principal axes of N- dimensional data elipse

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10 NEXTOR Factor Analysis with Two Variables

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12 NEXTOR Demand Normalization Deterministic Queuing Analysis Arrival Curve from ASPM OAG Departure Curve Based on Either Called AARs (Demand and Weather Normalization) Hypothetical Rates

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13 NEXTOR Deterministic Queuing Diagram

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16 NEXTOR Normalization for Conditions at other Airports Consider airports included in DFTI average For each compute daily average departure delay for flights not bound to LAX region Average airport departure delays using DFTI weights

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18 NEXTOR Performance Models Where: Y t is DFTI or DFTI component for day t; WX t is vector of weather factors for day t; DMD t is vector of demand factors for day t; ODEL t is average origin departure delay for day t; t is stochastic error term.

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19 NEXTOR Functional Forms Considered Parametric Linear (with 3, 6, 9, and 12 weather factors) Quadratic response surface Non-linear Non-parametric 9 clusters based on 3 weather factors 12 clusters based on 9 weather factors

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20 NEXTOR Linear Model Estimation Results (Pre-TTMA)

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21 NEXTOR Predicted vs Actual Values

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22 NEXTOR Outliers Used TMU logs to investigate days for which predictions have large errors Reasons for higher than predicted DFTI East flow Radar outages Air Force One Over-stringent ground delay program No clear explanations for lower-than- average DFTI: No news is good news

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23 NEXTOR DFTI Model Results

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24 NEXTOR Effect of TTMA at LAX Used same methodology but included TTMA (PFAST) dummy variable for Post 2/9/01 Used AARs in computing deterministic delay variable Considered both DFTI and Average Delay as Dependent Variables

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25 NEXTOR Delay Model Results

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26 NEXTOR URET Impact Performed Flight Level Analysis Observed Airborne Time for ASQP Flights for Corresponding Months Before and After URET (about 800,000 per analysis) Effects Estimated Before vs After URET Use URET Sectors vs Dont Use Use URET Sectors After URET Control for Distance, Direction, and Originating Airport and Destination Airport

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27 NEXTOR URET Model Results

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28 NEXTOR Conclusions A Priori Modeling without Ex Post Analysis is One Hand Clapping Normalization Required to Compare Observed with Predicted Outcomes and Account for Factors Beyond FAA Control We can Do This

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