After we have established that there are seats remaining on the flight after all O&D passengers have been seated (39 open seats in this case), we then ask the database to retrieve all flights from which a passenger on flight #1 can connect. For this example, the criteria for a connecting flight is one that departs between 30 minutes and 2.5 hours from the arrival time of the first flight. The two flights that are returned are flight #2 (STL-MCI) and flight #3 (STL-OKC). Each connecting market is then ranked by its average fare, since the airline will always prefer to seat the higher paying customer. In this instance, there are only two connecting flights available, flights #2 and #3, and the MDW-MCI route is the highest fare at $140 so those passengers will be accomodated first. By taking the daily demand derived from Table 5, and adjusting for time of day, frequency, and the connection, the total number of passengers who will fly between MDW and MCI is 31.29 passengers. Unfortunately, there are only 29.79 seats available flight #2 from STL-MCI, so we are limited to that number of passengers. Therefore:

Table 7 ? ?Passengers? table

By using this same methodology for the MDW-OKC market, we see that we can only fit another 1.2 passengers on Flight #3 due to the 50-seat capacity and 48.8 O&D passengers. This results in the following flight loads:

Table 8 ? Flight Loads

Revenue can then be easily calculated by multiplying the average fare by the average number of passengers for each market.

While this process of distributing demand is quite revolutionary to airline simulations, it does have some serious drawbacks. First, this system assumes that you know exactly how many passengers from each market will be willing to fly on your flight. This allows you to reserve only as many seats as you can fill, and does not take into account no-show passengers, demand spill, or demand variations due to seasonal fluctuations.

Secondly, the storage capacity required to handle such large files is immense. The O&D database available from the BTS itself is over 1.2 gigabytes, and the database required to store all market combinations could easily become as large. As well as capacity, server speed will be a large hurdle since these calculations, while small, will place a large strain on the server when repeated thousands of times.

With the generation of the hub and spoke system in the airline industry, it has become infinitely more difficult to distribute revenues over connecting flights. While airlines struggle with this problem, it is entirely possible for airline management simulations to use current technology to provide an accurate representation of what takes place. By using the preceding formulas to distribute demand and revenues to aircraft within the simulation, more accurate airline management simulations can be created to better train future managers, and to provide insight to the behaviors of such a market. Airline Empires, while already revolutionary with its competition-based demand equations, will have to develop this style of demand distribution to remain competitive in the coming years.

REFERENCES

Baldanza, Ben (2002). Measuring Airline Profitability. New York: McGraw-Hill: Retrieved from Handbook of Airline Economics pp 61-73.

Baseler, Randy (2002). Airline Fleet Revenue Management ? Design and Implementation. New York: McGraw-Hill: Retrieved from Handbook of Airline Economics pp 77-106.

Belobaba, Dr. Peter P. (2002) Airline Network Revenue Management: Recent Developments and State of the Practice. New York: McGraw-Hill: Retrieved from Handbook of Airline Economics pp 141-156

Bureau of Transportation Statistics. DB1BMarket database. Retrieved December 2, 2004 from http://www.bts.gov.

Caves, R. E. (1995). European airports and airline network strategies - their mutual relationship. United Kingdom: Loughborough University of Technology: Loughborough. Retrieved December 2, 2004, from Aerospace & High Technology Database database.

PETERS, H. J. (1975). Contribution to routing aircraft and to the economy of air transportation (11 Aircraft (MT); 3 Air Transportation & Safety (AH) No. ESA-TT-222; DLR-FB-74-25) December 2, 2004, from Aerospace & High Technology Database database.

PETERS, H. J. (1974). Routing of-aircraft and economics of flight operations [ph.D. thesis - tech. univ. stuttgart].DLR-FB-74-25Retrieved December 2, 2004, from Aerospace & High Technology Database database.

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