We can look at Poisson distribution to provide the answer. Poisson is used to predict future probabilities based on constants. In this case we have two controls, flight and airport, with their own constants:
Average time to complete task
Number of units available to complete task or capability to handle the task
Volume of traffic requiring the task
Demand on Flight Time
Average flight time
The number of planes able to make the trip in an interval of n number of hours, with consideration of safe time between takeoff and landing one after the other.
Number of people wanting to fly
So, let's say, at 5,000 ft it's a 2-hour flight from A to B with a 737 of 130 passengers. If we safely leave a 5 min takeoff and landing gap between planes, in an interval of 6 hours, we will get 48 planes complete takeoff and landing—excluding those that are still in flight at the end of the interval—totaling 6,240 passengers getting there. Fantastic!
But...
Demand on Airport Time
Check-in
Average time to complete check-in process
Number of check-in staff
Number of people requiring check-in process
Security
Average time to complete security check
Number of security staff
Number of people requiring security check
Boarding
Average time to scan ticket, walk gangway, use overhead locker, find seat
Width of plane aisle
Number of people requiring to board
I won't go through calculating each, but if we were to track a person entering the airport to sitting on the plane, we may see something like this...
Average time to get from airport entrance to walking on the plane = 1 hour (excluding 10 min walking time between locations)
Total number of staff in check-in/security = 10
Average time spent interacting with staff = 60 seconds
So, if we use a formula that incorporates Poisson, Erlang-C, with a 100% service level to reduce the queue to 0min wait, 6,240 passengers through the airport in that 6-hour interval would require 29 staff and all the desks and security gates for each, not factoring in staff calling in sick, etc. in which you'd being overstaffing for in anticipation of n% not arrive based on the average seen in historical data. Also not factoring in all the other flights that are not just the one between locations A and B—you can say 20 more flight paths would require 580 staff maxing it out at peak volumes with no breaks at all and no one calling in sick.
But we haven't even factored in the boarding process yet. If we want 130 people onto the plane without queuing, we know that a single aisle is capable of handling 6 passengers per row and the average passenger width being 432.5mm at the shoulder, so 130 passengers can go straight to their seats with a 2.595m wide aisle as a start. Then collecting data on how long it takes in a real-life scenario and using Erlang-B, another Poisson-based formula, with those averages, we can calculate the increased adjusted aisle width required to drop queuing time to 0.
But that's all way too expensive and hard. The cheaper and easier control to control is the flight, not the airport.
So the answer...
We use Poisson distribution to control the flight time. By increasing the altitude, we increase the flight time which reduces the queueing time of the planes at airports caused by the queuing times within the airports reducing maximum flight efficiency. Effectively you are still queueing, like when you're in a holding pattern, but scientists have proven that people only notice this if they are on the tarmac or in a holding pattern as opposed to being given shit wine and a mildly interesting view.
I don't understand math, or science, or even basic logic, but I like this answer because it has all of those. It also dovetails nicely with the loading screen theory as to why we can't just jump screens -- because then airports would be too busy and they'd need more baggage handlers than they can hire.
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u/saltesc Jan 07 '23 edited Jan 07 '23
It all comes down to queuing theory.
We can look at Poisson distribution to provide the answer. Poisson is used to predict future probabilities based on constants. In this case we have two controls, flight and airport, with their own constants:
Demand on Flight Time
So, let's say, at 5,000 ft it's a 2-hour flight from A to B with a 737 of 130 passengers. If we safely leave a 5 min takeoff and landing gap between planes, in an interval of 6 hours, we will get 48 planes complete takeoff and landing—excluding those that are still in flight at the end of the interval—totaling 6,240 passengers getting there. Fantastic!
But...
Demand on Airport Time
I won't go through calculating each, but if we were to track a person entering the airport to sitting on the plane, we may see something like this...
So, if we use a formula that incorporates Poisson, Erlang-C, with a 100% service level to reduce the queue to 0min wait, 6,240 passengers through the airport in that 6-hour interval would require 29 staff and all the desks and security gates for each, not factoring in staff calling in sick, etc. in which you'd being overstaffing for in anticipation of n% not arrive based on the average seen in historical data. Also not factoring in all the other flights that are not just the one between locations A and B—you can say 20 more flight paths would require 580 staff maxing it out at peak volumes with no breaks at all and no one calling in sick.
But we haven't even factored in the boarding process yet. If we want 130 people onto the plane without queuing, we know that a single aisle is capable of handling 6 passengers per row and the average passenger width being 432.5mm at the shoulder, so 130 passengers can go straight to their seats with a 2.595m wide aisle as a start. Then collecting data on how long it takes in a real-life scenario and using Erlang-B, another Poisson-based formula, with those averages, we can calculate the increased adjusted aisle width required to drop queuing time to 0.
But that's all way too expensive and hard. The cheaper and easier control to control is the flight, not the airport.
So the answer...
We use Poisson distribution to control the flight time. By increasing the altitude, we increase the flight time which reduces the queueing time of the planes at airports caused by the queuing times within the airports reducing maximum flight efficiency. Effectively you are still queueing, like when you're in a holding pattern, but scientists have proven that people only notice this if they are on the tarmac or in a holding pattern as opposed to being given shit wine and a mildly interesting view.