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Linear regression is the primary data analysis method used in the development of air traffic delay models. When the data being studied does indeed have an underlying linear model, this approach would produce the best-fitting model as expected. However, it has been argued by ATM researchers [Wieland2005, Evans2004] that the underlying delay models are primarily non-linear. Furthermore, the delays being modeled often depend not only on the observable independent variables being studied but also on other variables not being considered. The traditional regression approach alone may not be best suited to study these type of problems. In this paper, we propose an alternate methodology based on partitioning the data using statistical and decision tree learning methods. We then show the utility of this model in a variety of different ATM modeling problems.

A number of airlines have FOQA programs that analyze archived flight data. Although this analysis process is extremely useful for assessing airline concerns in the areas of aviation safety, operations, training, and maintenance, looking at flight data in isolation does not always provide the context necessary to support a comprehensive analysis. To improve the analysis process, the Aviation Data Integration Project (ADIP) has been developing techniques for integrating flight data with auxiliary sources of relevant aviation data. ADIP has developed an aviation data integration system (ADIS) comprised of a repository and associated integration middleware that provides rapid and secure access to various data sources, including weather data, airport operating condition (ATIS) reports, radar data, runway visual range data, and navigational charts.