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Queue management prediction
As shown in Figure 17 - one for each flight - in order to improve results for both peak and off- peak hours, the team created 20 separate sub- samples. Group AirportDFM more appropriate models and wind direction implied by the same set of test data queue management generated during the days of the runway configuration into account. As a result, a 7 % decrease in the average absolute error, and the actual ( number 18 ) was observed 2.5 % increase in the number of taxi predicted that in 5 minutes. Using the results of the data collection in July -August data set was in agreement with the results obtained. Sample order to observe the relationship between the size of the array and the taxi clearance delays.
Each sub- sample results were quite stable. And the ability to balance the need to increase the size of the array when it is much less predictable since the bread and taxi times, until it reaches a saturation point, the passing rate of departures increased without problems. Saturation point with the size chart below, departing planes taxi queue management unnecessary congestion and excessive delays ( Appendix E Figure 9 ) can be avoided. The concept of levels in order to control the aircraft rolling into a taxi and was the basis for the development of a system to reduce variation. Airport taxi as fast or slow speeds for each category of staff models the effect of increasing or decreasing all along the system.
Between 5 knots and 20 knots taxi speeds due to an increase or decrease in queue management the taxi fuel burn fraction of the time and the models were very consistent. Assumptions and allows us to see the results of the same model allows for variation in the speed of the taxi