Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
⢠The tallest tail height in the ADG was used to characterize the taxiing aircraft as an obstacle. ⢠The widest wingspan in the ADG was used to characterize the dimension of the approaching aircraft. ⢠The taxiing aircraft was assumed to be a fixed object; how- ever, for many airports, a taxiing aircraft will not be present during most of the landings. ⢠A missed approach rate of 1 percent was kept for the calcu- lations. Based on the latest FAA data, even a 0.2-percent rate is conservative (FAA, 2008). Runway Veer-Off Models for Landing and Takeoff During the landing, after touchdown, or during the takeoff roll, the pilot may lose directional control. Some common causes and contributing factors include low runway friction, mechanical failures, and adverse weather conditions. The basis of the approach used in this study is the probabil- ity of aircraft runway excursions and the risk that an aircraft will stop outside the boundaries of the existing or planned RSA. The approach to model risk of collisions is accomplished by using a combination of frequency and location models. In a sense, the modeling considered the bounds of the RSAs rather than the presence of obstacles in the vicinity of the RSAs or the aircraft speed when striking obstacles. While the difference makes the new models simpler, the approach can be extended to consider risk if this type of analysis is required. The two- part model approach is represented in Figure 18. Event Probability (Frequency Model) The likelihood of an aircraft veer-off incident depends on operational conditions and human factors. It includes airport characteristics, weather conditions, and aircraft performance, as well as the relationship between the runway distance required by the aircraft for the given conditions and the runway distance available at the airport. Similar to the approach presented in ACRP Report 3 (Hall et al., 2008), backward stepwise logistic regression was used to calibrate the veer-off frequency models. Data were gathered from accidents and incidents and NOD. To avoid the negative effects of multicolinearity on the model, correlations between independent variables were tested to eliminate highly correlated variables, particularly if they did not contribute significantly to explaining the variation of the probability of an accident. The selected approach can identify relationships missed by forward stepwise logistic regression (Hosmer and Lemeshow, 2000). The predictor variables were entered by blocks, each con- sisting of related factors, such that the change in the modelâs substantive significance could be observed as the variables were included. The basic model structure used is logistic, as follows: where P{Accident_Occurrence} is the probability (0â100%) of an accident type occurring given certain operational conditions, Xi are independent variables (e.g., ceiling, visibility, cross- wind, precipitation, and aircraft type), and bi are regression coefficients. Several parameters were considered for inclusion in the models. The backward stepwise procedure helps identify the most relevant variables for each type of event. One major improvement relative to models presented in previous stud- ies was the use of tailwind and headwind. These variables were not present in the overrun and undershoot models presented in ACRP Report 3 (Hall et al., 2008) because the actual run- way had not been identified in the NOD. The research team gathered information on the runways used, and the process allowed the calculation of the head/tailwind components of the model. Another major accomplishment that has increased model accuracy was the inclusion of a runway criticality factor. The new parameter represents the interaction between the run- way distance required by the aircraft and the runway distance available at the airport. The logarithm of the ratio between the distance required and the distance available was used to P Accident Occurrence eb b X b X b X _{ } = + + + + 1 1 0 1 1 2 2 3 3+K 23 Event probability Location probability Probability Operating Conditions (airport, type of operation, runway distances, weather, aircraft performance) RSA configuration, location of obstacles, airplane wingspan the RSA Two-Part Probability Model conditions cr ft wingspan Veer-off and depart Figure 18. Modeling approach.
