A Statistical Learning Approach to the Modeling of Aircraft Taxi-Time

Abstract

Modeling aircraft taxi operations is an important element in understanding current airport performance and where opportunities may lie for improvements. A statistical learning approach to modeling aircraft taxi time is presented in this paper. This approach allows efficient identification of relatively simple and easily interpretable models of aircraft taxi time, which are shown to yield remarkably accurate predictions when tested on actual data.

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Document Details

Document Type
Technical Report
Publication Date
Aug 10, 2010
Accession Number
ADA533367

Entities

People

  • Mariya A. Ishutkina
  • Richard Jordan
  • Tom G. Reynolds

Organizations

  • Federal Aviation Administration

Tags

Communities of Interest

  • Air Platforms
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Air Traffic
  • Aircrafts
  • Airports
  • Algorithms
  • Artificial Intelligence
  • Congestion
  • Crossings
  • Data Processing
  • Data Sets
  • Information Science
  • Pattern Recognition
  • Regression Analysis
  • Standards
  • Time Intervals
  • United States Government
  • Vehicles

Readers

  • Aviation Safety and Air Traffic Management
  • Computational Modeling and Simulation
  • Neural Network Machine Learning.