# Optimizing Flight Delay Predictions with Scorecard Systems
## 4 Research Method
### Database  
The dataset involved the collection of historical flight data in the U.S., containing information such as departure and arrival times, route, aircraft type, and other factors that can affect delays. An exploratory analysis of the data was then conducted to identify key factors that affect delays. The next step was the construction of scorecards, which involved assigning weights to individual factors based on their impact on delays, normalizing and scaling these factors to facilitate the interpretation of the results. The efficacy of the scorecards was validated utilizing test data, and the outcomes were assessed by juxtaposing the forecasted delays with the actual data. Finally, a validation and analysis of the results was carried out to assess the accuracy and reliability of the model. Once the data were collected, an exploratory analysis was conducted to identify the key factors influencing flight delays. This analysis included the following:  
* Examining the distributions of individual variables (Table 1). * The analysis of correlations between variables and delays. * Identifying data gaps and possibly filling or removing them. * Grouping flights by categories such as aircraft type, route length, or departure time to understand which groups are most susceptible to delays. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Mean** & **Std. Dev** & **Skewness** & **Kurtosis** & **Min** & **Max** \\ \hline Year & 2018 & 3.129 & \(-\)0.380 & \(-\)0.892 & 2012 & 2023 \\ Month & 6.529 & 3.793 & \(-\)0.229 & \(-\)1.274 & 1 & 12 \\ DayOfMonth & 15.988 & 8.988 & \(-\)0.043 & \(-\)1.248 & 1 & 31 \\ DayOfWeek & 4.224 & 2.051 & \(-\)0.301 & \(-\)1.263 & 1 & 7 \\ DepTime & 1334.590 & 316.905 & \(-\)0.303 & \(-\)0.269 & 715 & 2244 \\ DepDelay & 9.915 & 126.715 & \(-\)6.143 & 50.193 & \(-\)16 & 1405 \\ ArrTime & 1635.471 & 374.279 & 0.963 & \(-\)1.582 & 1 & 2348 \\ ArrDelay & 8.125 & 126.170 & 0.498 & \(-\)0.182 & \(-\)115 & 1315 \\ Distance & 882.217 & 482.217 & \(-\)0.577 & \(-\)0.379 & 205 & 3715 \\ CarrierDelay & 30.070 & 89.173 & 4.864 & 29.255 & 0 & 1211 \\ WeatherDelay & 3.325 & 32.549 & 14.205 & 29.634 & 0 & 1401 \\ NASDelay & 17.453 & 36.529 & 6.446 & 29.536 & 0 & 392 \\ SecurityDelay & 0.104 & 0.104 & 0.013 & 0.073 & 0 & 112 \\ LateAircraftDelay & 44.297 & 85.173 & 8.210 & 104.386 & 0 & 1404 \\ \hline \hline \end{tabular} Source: Authors’ own elaboration.  
\end{table} 
Table 1: Basic information about the investigated data. The results of the exploratory analysis helped to select the key variables that were used in the construction of the scorecards. It also allowed us to understand the scale of the problem and the specific relationships between delays and various operational and external factors. Table 2 categorizes the primary causes of flight delays measured in minutes. Each type of delay reflects a specific reason contributing to overall flight disruptions:  
* CarrierDelay: Delays caused by airline-related issues such as maintenance problems or crew unavailability. * WeatherDelay: Delays resulting from adverse weather conditions that affect flight operations. * NASDelay: Delays attributed to the National Air System including air traffic control and airport congestion. * SecurityDelay: Delays due to heightened security checks or airport security incidents. * LateAircraftDelay: Delays caused by the late arrival of the incoming aircraft intended for the next flight segment. Before presenting any scorecard, it is imperative to examine the causes of flight delays based on the provided data. Causes were collected from 2003 to 2006. There were 7,030,910 selected flights with delays of more than 60 min. Using separate variables, the minutes of delay associated with the cause of delay are provided for departure delay, carrier delay, weather delay, security delay, late aircraft delay, and NAS delay. Sometimes there was a positive-only departure delay--722,205 cases. All minutes were summarized and the share of total minutes for arrival delay is calculated as a percentage.