Airline Empires is written in the PHP programming language and currently uses the open-source database program MySQL to store the large amounts of information. Both of these development tools are known for their speed and performance, which is critical when choosing a programming language and database program for a complex program, such as this. In order to store the information into the MySQL program, a simple database schema needs to be set up. While this example will use MySQL as the database, the schema is easily transferred to any database program available.

In order to record the revenues for each passenger group, we will need four tables: one to store the aircraft information, such as speed, seats, and cost, which we will call the ?aircraft_static? table, since it will not need to be changed once it is created, one to store the individual aircraft information such as owner and age called ?aircraft?, one to store the individual flight information such as the city pairs, time, and cost called ?flights?, and one to store the passenger revenue data which we will call ?passengers.? The important thing to notice is that all costs are calculated and stored separately from the revenues. This satisfies the earlier criteria of accurately reporting revenues.

To better illustrate how the system will work, an example is necessary. First we will determine the passenger loads for a non-stop and connecting flight with no competition. Airline ?X? operates out of four cities, Chicago Midway (MDW), St. Louis (STL), Kansas City (MCI), and Oklahoma City (OKC) with a hub in STL. There are three flights we will use for this example, and the information entered into the ?flights? table of the database will resemble the following:

Table 4 ? ?Flights? table

Aircraft #1 is a 100-seat aircraft, while aircraft #2 has 50 seats. The O&D demand between the individual markets will be:

Table 5 ? Hypothetical O&D demand and average fares derived from BTS database

The first task of the revenue program is to find the O&D passengers traveling non-stop on each flight. For flight #1 (MDW-STL) we will need to take the daily O&D passengers between the two cities, and adjust them for time of day, frequency, and connections which we designated in tables 1, 2, and 3. By interpolating between the departure (10%) and arrival (14%) percentages, we can find a percentage of the daily O&D passengers willing to fly on this flight at 12% of the original 500 daily passengers, or 60 passengers. Since there is only one frequency available during the day, we then reduce the 60 passengers by 15% which leaves us with 51 passengers. Next, by adjusting for the nonstop reward of + 20%, we have a final count of 61 passengers who will board flight #1 in MDW bound for STL. Since there are 100 seats available, we can carry all 61 passengers. We repeat the same formula through all non-stop flights, careful to ensure that no more than the capacity of the aircraft are allowed. This results in the following:

Table 6 ? ?Passengers? table

Once all O&D passengers have been placed, we need to calculate remaining capacity for connecting passengers. Once again, we will use flight #1 to take a close look at the details of how to establish how many passengers will choose to connect through STL on the MDW-MCI and MDW-OKC markets.