Figure 5: Summary Data
alone, one can see that the simulation predicted large delays under certain circumstances, but that minimal delays and few re-requests were likely for more typical, small-airport characteristics.
An ANOVA analysis of the data showed significant correlation between a few independent variables (e.g. % GA) and important measured responses (e.g. airport utilization and average wait time), but the effects of λ, the inter-arrival time, were strongly significant and dominated the effects of the other variables. This result confirms what one might intuitively expect: as the average inter-arrival period nears the average operation duration, the queues in the system begin to build dramatically. The relatively mild correlation between the other variables and the performance of the system implies that wide latitude can be taken in the design of a particular AACV without adversely affecting the performance outcome. It was also apparent from the simulation results that the system is robust enough to handle occasional traffic spikes that might be expected to occur even at a low-use airport.
The model (see Figure 6) of Utilization= .(%GA, λ, request response time) had F=82.16, p.<.0001 showing significance at the 0.05 level. The request response time proved to be a small effect as compared to the environmental factors of traffic mix and rate of arrivals.
The model (see Figure 7) Average Delay= .(%GA, λ) had F=28.53, p.<.0001 showing significance at the 0.05 level.
One other observation warrants mention: the operation time for each priority aircraft’s use of the ACV was estimated using a simplified path predicated on random position assignment and IAF selection. The only independent variable directly influencing the time to fly a specified path is the assigned type (and therefore speed profile) as determined by the independent variable %GA,
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本文鏈接地址：A METHOD OF SEPARATION ASSURANCE FOR INSTRUMENT FLIGHT PROCE(14)
