In most airline simulations, and in a majority of the airlines themselves, it is attempted to compare profitability of a flight by comparing the revenue from the tickets sold to the operating and fixed costs of the operating flight. When operating in a solely origin and destination (O&D) market, this is very simple since all revenues are associated with that one flight. Where the equation becomes complex is when you factor in connecting passengers. The problem is how do you prorate the ticket to find what percentage of the revenues are designated to which flights (Baldanza, 65) . Simply dividing the revenue by the number of flights is not enough since a fare of $1,000 to fly from Louisville to London, England can hardly be divided into two $500 segments. Likewise a distance proportion is not entirely accurate either since several ticket prices are set according to O&D markets, and while distance is a factor, it is not the most driving factor in determining the fare. One solution is to separate costs and revenues by reporting costs per flight, and revenues per city pair. While this is a more accurate way to determine total profitability of an airline, it makes it difficult to view the profitability of a single flight. Fortunately, this is not as central to a simulation since we do not necessarily need to separate revenues per flight (Baseler, 80).

The first step in achieving this new, more accurate revenue generating system, is to find actual (or at least representative) O&D data. Since the only reliable O&D data is available from the BTS, only U.S. cities will be reported. With the O&D data, we no longer need to find a weighted average of passenger enplanements at both cities, rather we now have a representation of the total number of passengers traveling on that distinct market.

Since the O&D data does not discriminate regarding time of day, it is necessary to divide the passengers according to the time the flight operates. An accurate representation of this was achieved by designating a percentage of the daily O&D passengers grouped by timeframe.

Table 1 ? O&D distribution throughout the day

Another factor to the accurate assignment of revenues is to adjust for the frequencies offered. For this a deviation percentage from the original demand is used.

Table 2 ? Demand deviation due to frequency

This takes into account the passengers preference for a flight convenient to the time they want to depart. The higher the frequencies, the more likely you will offer a convenient flight to the passenger, and more passengers will choose to fly your route. This is the driving force behind the ?S-curve,? and its requirements are satisfied through the above deviations.

Another factor to include when determining how many passengers will fly on a particular flight is whether or not the flight is a non-stop flight. Since passengers tend to prefer non-stop over connecting flights, a deviation cumulative to the frequency deviation can be developed.

Table 3 ? Demand deviation due to number of connections

This accommodates the notion that a passenger will choose an available non-stop with all other factors equal at least 40% of the time over a two connection trip. Other factors that can be taken into consideration are the passengers preference for jets over turboprops, amenities, airport lounges, etc. For the sake of simplicity in the following example, we will only use the two deviations.