Modeling Flight Delays and Cancellations at the National ... · Modeling Flight Delays and Cancellations at the National, Regional and Airport Levels in the United States Banavar

Modeling Flight Delays and Cancellations at the National, Regional and Airport Levels in the

United States

Banavar SridharYao WangAlex Klein

Richard Jehlen

National Airspace System Performance WorkshopAsilomar, CA

April 13-16, 2009

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Motivation

• Weather is the major cause of delay in the National Airspace System (NAS)

• Four possible scenarios

• Relate delay, cancellations and other NAS performance metrics to the weather conditions to improve Traffic Flow Management

PoorWeatherForecast

AccurateWeatherForecast

Poor operationalresponse

X X

Properoperationalresponse

X Bestoutcome

Results

• Developed flight delay and cancellation models at the national, regional and airport levels

• Expected number of aircraftimpacted by weather goodproxy for delay

• Different models for summer and winter• All metrics can be estimated to same level of accuracy• FAA (ASPM and OPSNET) databases are complementary• Neural Network models perform slightly better

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Outline

• Objectives • Databases• Airspace Performance Metrics• Modeling/Estimation of Metrics

– Regression Models– Neural Network Models

• Results• Conclusions

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Objectives

• Develop NAS performance metric models based on FAA operational traffic databases– Different metrics– Impact of databases – Approach

• Linear regression models• Neural networks models

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Databases

• FAA Operations Network (OPSNET)– Data available from 1990– Daily values– 45 airports– Total national delay

• Aviation System Performance Metrics (ASPM)– Data available from 2000– Every 15 minutes– 75 airports– Total OAG-based and flight-plan based arrival delays,

EDCT hold minutes, airborne delay, flight cancellations• Paper uses data from 2005-2008

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NAS Performance Metrics

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Weather Impacted Traffic Index (WITI)

Severe weatherAircraft positions

Weather Impacted Traffic Index (WITI)

• Grid-based WITI• National Weather Index (NWX)

– En-route WITI (E-WITI), representing convective weather impact on major flows between city pairs

– Terminal WITI (T-WITI), representing weather impact on major airports

– Airport Queuing Delay (Q-Delay), representing surface and terminal-airspace weather impact on major airports in a non-linear fashion

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• Number of aircraft affected by weather ( )• Number of aircraft affected by weather in each

Center ( )• Performance metric ( ) • Models

– Linear Regression (LR)

– Multiple Linear Regression (MLR)

– Neural Networks

– Dynamic Models

Modeling/Estimation of Metrics

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Performance of Regression Models

OPSNET Total Delay Flight Cancellations

Estimate

Error

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Performance of Regression Models

• Regression models perform a good job of accounting for the impact of weather on delays and flight cancellations

• For systems with demand-capacity imbalance, growth in delay is non-linear

Atlanta Airport

Nonlinear Models

Inputs PerformanceMetric(Target)

Performance of National Model

14• Neural Network models perform slightly better

Performance of different WITI definitions

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• Models using NWX perform slightly better• Difference not significant while using MLR or NN

Seasonal performance of national delay models

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• Higher correlation during summer• Lower correlation in winter may be due to higher number ofcancellations on days with heavy snow, very low ceilings/visibility

Airport delay models using Regression analysis

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• 34 major airports on the OEP-list

• Good delay estimates for ORD, ATL,..• Delay at ten airports in Eastern U.Snot influenced by NWX in the neighboring Centers

Behavior of airport models

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OPS – Delayed & On-Time Gate Arrivals Last 8 Days Holding Minutes Last 8 Days

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Concluding Remarks

• Estimation/Modeling of performance metrics resulting from the use the two databases are comparable

• For all metrics, neural networks produce higher correlation and reduced errors than regression methods

• Different methods of reducing neural network complexity produce similar results