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7.4 Combining Delay and Delay Impact Models
The classification power of the Delay model is approximately 74 percent with more than 80 percent of correct predictions of days with the occurrence of a weather delay at Heathrow airport. The classification power of the PDF Model is approximately 84 percent. These models are combined by calculating the conditional probability of a weather delay occurring within the specific PDF category. The classification power of the combined model, which aims to predict weather delays at Heathrow airport from existing weather conditions, is presented in Table 7-12. Approximately 50 percent of days within the sample are correctly classified. This suggests that direct prediction of weather delay is possible with limited accuracy and that use of two separate models is preferred rather than the use of the combined model.
Table 7-12. Combined model classification power statistical analysis, the hypothesis that weather parameters do not have an impact on air traffic delays has been rejected. Further statistical analysis highlighted the most important weather parameters that are affecting Heathrow delays. In order to enable estimation of the impact of future changes in weather patterns on air traffic delays, two different models of weather related delay have been developed.
However, some concerns regarding the data sample used for modelling, should be mentioned. First, statistical analysis was performed using aggregate daily weather-delay data. Most of the continuous weather predictors were averaged daily values, while four weather parameters were expressed as dummy variables. Therefore, it was not possible to capture changes in weather parameters throughout the day. As a result,
Chapter 7 Heathrow weather delay analysis
sample characteristics were not adequate for a microscopic analysis and quantitative modelling of airport delays, but only for a categorical analysis of days with a weather-delay occurrence.
Further, the weather-related delay analysis of Heathrow airport (Chapter 6) suggests problems with a lack of reporting. There is a possibility that not all weather-related delays are reported. As a result, the existence of unreported delays may distort (i.e.
reduce) the values for the percent of flights delayed. In addition, CODA records account for only a subset of airlines that are currently involved in the reporting scheme (Table 6-1). There is a possibility that additional non-reporting airlines might affect the increase of overall duration of weather delays by decreasing the airport capacity.
However, there is also a possibility that the existence of non-reporting flights might actually decrease the reported weather delays since airlines might “blame” the airport capacity instead of weather conditions. Overall, since non-reporting airlines’ flights are not included in the total number of flights provided by CODA there is a possibility that inclusion of an airport capacity variable might improve the models. In the PDF model, the OnlyWeather and OtherDelay dummy variables were used to inform the model about the existence of other problems at the airport, thereby controlling for other capacity constraints.
As mentioned previously, the main objective of this Chapter was to analytically identify weather parameters that influence the occurrence of weather related delay at Heathrow and the level of their impact. This has been statistically tested using logistic and ordered probit regression techniques. Overall hypothesis testing for both models shows that weather conditions do have an impact on air traffic delays at Heathrow airport. In addition, the models have managed to highlight the predictors or weather parameters whose influence on airport delay is statistically significant.
The statistical analysis of the Delay Model highlighted 12 out of 14 variables as statistically significant (Table 7-5). In addition, the model’s classification power (Table 7-6) suggests that the weather delay occurrence at Heathrow airport can be explained by the model using proposed weather predictors (Table 7-1). In addition, the Delay Model highlighted that the existence of thunderstorms, snow, and fog increases the chance of a weather delay occurrence by more than 25 percent. This suggests that these three conditions present a threat to Heathrow in the future. Furthermore, the model suggests that one unit change in the wind speed and visibility around the airport influence the existence of weather-related delays by approximately 8 and 6 percent respectively.
Similarly, the model of percentage of flights delayed had good classification power and a potential to predict the percentage of flights delayed due to expected weather conditions. The statistical analysis of the PDF model highlighted nine significant weather-delay predictors (Table 7-9). The model can correctly classify, using these nine predictors, approximately 84 percent of the daily weather delays. However, although the overall classification power of the model is fairly good, there are some concerns with underestimates of extreme delays (i.e. PDF>15%). The classification power for these is only 0.2 percent of the overall sample, or approximately 11 percent within the category (Table 7-10). Although it is important to acknowledge the model limitations to assess future impacts, the frequency of this category (less then 2 percent) suggests that these events are very rare. In addition, similarly to the Delay Model, thunderstorms, wind speed and visibility were again found to be the most influential factors affecting weather delay at Heathrow.
Both models would probably have captured a rather higher dependence on icing conditions if some type of derived/joint variable capturing these had been used (for example, identifying periods with minimum temperature <3C and with precipitation). On the other hand, without such a variable, independently modelling changes in precipitation and temperature (e.g. in “passive” ranges above 3C), would seem to underestimate their contribution to the high proportion of delays attributable to de-icing.
To summarise, both models have shown fairly good modelling power and the ability to predict weather-related delays at Heathrow airport. In addition, both models have managed to highlight the most influential weather-related delay predictors for Heathrow airport and to statistically determine their level of impact. However, the combined model of weather-related delays revealed some concerns. Although the individual models have classification powers of 74 and 84 percent respectively, the combined model classification power is approximately 50 percent. This finding indicates a potential problem with the Delay Model. Although it correctly predicts 84 percent of delay days (Table 7-6), its classification power drops when predicting days without delays (to 60 percent). For that reason it is better to use the Delay and Delay Impact models separately.
In general, weather conditions are found to be a major factor affecting delays at Heathrow airport. In addition, the impact of specific weather conditions can be statistically estimated. Based on the prediction power of these models, they can be used to forecast the probability of weather delay events and the percent of flights that could be delayed in the future. This will be done in Chapter 9.
Chapter 7 Heathrow weather delay analysis