This information describes the characteristics of the airport at which the incident occurred. In general, this information will stay the same from incident to incident at the same airport45 so most of the interesting variation in these variables is between airports. The conclusions are therefore more useful when comparing different types of airports.
(ATQA OE)
This variable indicates if special procedures were in effect at the time of the incident. Figure 14 presents the distribution of this variable.
Figure – Distribution of Special Procedures in Place
Future Research
Table 66 and Table 67 reports the breakdown of this variable by severity and the results of Fisher’s Exact test. Note that this variable can only be examined among OE incidents. The test statistic indicates that special procedures have no effect on the severity of an incident. While there is no impact on severity, no information can be gleaned about the impact on frequency of incursions while special procedures are in effect.
Table – Observed Distribution of Special Procedures in Place by Severity
|
A
|
B
|
C
|
D
|
Total
|
No
|
39
|
36
|
756
|
96
|
927
|
Yes
|
9
|
3
|
88
|
6
|
106
|
Total
|
48
|
39
|
844
|
102
|
1,033
|
Table – Expected Distribution of Special Procedures in Place by Severity
|
A
|
B
|
C
|
D
|
Total
|
No
|
43
|
35
|
757
|
92
|
927
|
Yes
|
5
|
4
|
87
|
10
|
106
|
Total
|
48
|
39
|
844
|
102
|
1,033
| Traffic Complexity Code
(ATQA OE)
This variable indicates the complexity of traffic at the time of the incident on a five-point scale. This variable originates from the ATQA OE database and only applies to OE incidents. Figure 15 presents the distribution of hourly ops by complexity code. Higher complexity is associated with higher hourly operations. Recall that hourly operations are not entirely accurate and the extreme outliers likely represent data problems rather than actual observations. Regardless, the graph shows a distinct trend in median operations by complexity level. However, the degree to which the distributions overlap in the middle categories suggests that the definition of complexity may not be entirely clear in that region (or at least not entirely defined by hourly operations).
The positive correlation between complexity code and hourly operations is not visible for OEP 35 airports. There is a slight trend in median hourly operations, however the overlap between categories is much more pronounced. It is also helpful to keep these values in mind when examining the results presented below.
Figure – Distribution of Hourly Operations by Complexity Code, OEP 35 versus Non-OEP 35
Figure – Distribution of Traffic Complexity Code
Figure 16 presents the overall distribution for complexity code. Table 68 and Table 69 present the distribution of responses by severity category and the results of a Chi-Squared test.
Table – Observed Distribution of Traffic Complexity by Severity
|
A
|
B
|
C
|
D
|
Total
|
Low
|
11
|
8
|
250
|
65
|
334
|
Low-Mid
|
3
|
8
|
160
|
17
|
188
|
Average
|
22
|
14
|
248
|
17
|
301
|
Average-High
|
7
|
7
|
144
|
2
|
160
|
High
|
5
|
2
|
42
|
1
|
50
|
Total
|
48
|
39
|
844
|
102
|
1,033
|
Chi2 score: 70.79
|
Degrees of Freedom: 12
|
P-value: 0.00
|
Table – Expected Distribution of Traffic Complexity by Severity
|
A
|
B
|
C
|
D
|
Total
|
Low
|
16
|
13
|
273
|
33
|
334
|
Low-Mid
|
9
|
7
|
154
|
19
|
188
|
Average
|
14
|
11
|
246
|
30
|
301
|
Average-High
|
7
|
6
|
131
|
16
|
160
|
High
|
2
|
2
|
41
|
5
|
50
|
Total
|
48
|
39
|
844
|
102
|
1,033
|
It appears that category D incidents are observed more than expected for low complexity, while the conflict events are observed more frequently than expected for average complexity. Category C incursions appear more often than expected for all levels of complexity except the lowest. This suggests that increased complexity is associated with increased severity.
Future Research
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Refine and clarify traffic complexity measures
There is a variety of other problems associated with the interpretation of this variable. First and foremost, this is a purely subjective measure. The reporting form offers no guidance on what constitutes “average” or “high” complexity so interpretations of “high” complexity may differ person-to-person or day-to-day. Secondly, due to the lack of guidance, the measure is poorly calibrated. For example, “average complexity” may refer to what is average for a given tower, average for a time of day, or average across the entire NAS. Thus, even though someone may report “average” complexity, it is difficult to tell what the comparison is (i.e., “average” relative to what?). Thirdly, without normal operations it is difficult to discern the true impact of this variable; that is, it is possible that incursions themselves are more likely in high complexity times even if it does not affect the distribution of severity. It is possible that high complexity occurs twice as often for incursion events as for normal operations. Nevertheless, the results in Table 68 indicate that there is a relationship between complexity and incident severity.
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