# Optimizing Flight Delay Predictions with Scorecard Systems
## 3 Classification of Flight Delays  
In the United States, there is no unified federal regulation that clearly defines the thresholds for various types of flight delays or mandates standardized passenger compensation. As a result, airlines and researchers often rely on alternative classification schemes when analyzing delay events. In this study, we adopt a structured delay categorization model inspired by the European Union's Regulation (EC) 261/2004, which outlines consistent criteria for delay compensation, cancellations, and denied boarding across member states.  
Based on this regulatory framework and previous academic sources, we distinguish the following delay statuses [12]:  
* Early arrival-when the aircraft lands before its scheduled arrival time.
* On time-when arrival occurs at or within 15 min of the scheduled time.
* Delayed-when the delay ranges between 15 min and 2 h.
* Significantly delayed-when the delay exceeds 2 h.
* Exceptional delay-when the delay surpasses 6 h or is otherwise categorized as extreme.  
These categories are translated into a five-level scale, with Level 1 representing early arrivals and Level 5 indicating extraordinary disruptions.  
In addition to categorizing delay severity, the U.S. Department of Transportation (DOT) provides an operational classification of delay causes. These include the following:  
* Carrier-related delays, such as technical issues, ground operations, or overbooking.
* Late-arriving aircraft, where previous flight delays cascade into subsequent segments.
* National Airspace System (NAS) delays due to air traffic control restrictions, limited runway availability, or moderate weather.
* Security-related delays, resulting from events such as passenger screening bottlenecks or terminal evacuations.
* Weather delays, caused by severe meteorological conditions that impact flight safety.  
Each cause is considered independently and contributes a measurable portion to the total delay time. By breaking down delays into component parts, it becomes possible to analyze the cumulative impact and explore causal relationships.  
Analyzing the underlying causes of flight delays is essential for generating actionable data-driven insights and building reliable predictive models. The United States, as the largest aviation market--handling around 30% of global air traffic and possessing the highest number of operational airports--provides a uniquely rich dataset. Since 1987, detailed flight-level records have been systematically published by the Bureau of Transportation Statistics, offering researchers access to a long-term, high-volume data environment.  
For the purposes of this study, we focused on a recent and representative time window: January 2018 to December 2022. This five-year period includes more than 30 million domestic flight records, which were obtained in CSV format directly from the Bureau's online repository.  
To handle the large data volume efficiently and ensure accurate processing, the raw files were imported into SAS 9.4 software, which is optimized for large-scale statistical computing and enables the processing of datasets that far exceed the row limitations of spreadsheet applications. In addition, custom-built analytical scripts and data transformation procedures developed by the authors were employed to clean, integrate, and restructure the data. This approach allowed for scalable computation, exploratory analysis, and the implementation of scorecard-based modelling on a robust technical foundation.