A Preliminary Analysis of
the Evolution of U.S. Air Transportation Network
How does the air traffic network evolve over time? Is there any pattern
to how the air
traffic network evolves with respect to the size of the markets, type
of competition,
geospatial features, and structural changes following the events of
September 11, 2001
(9/11)? Are the changes transitory or permanent in nature? Can we lay
out the trajectory
possibilities of the network and determine factors influencing them?
The National Airspace System (NAS) in the United States US) is structured
primarily
around a web of air transportation markets linked to each other through
a network of
465 commercial airports located in and around 363 metropolitan statistical
areas (MSAs).
The total number of origin-destination (O&D) markets in the NAS
ranges somewhere
between 36,000-40,000 pairs depending upon seasons and economic
cycles. In its present
structure, these markets are hierarchical; a small number of markets
account for the largest
number of passengers and, hence, air traffic flows. For example, there
were approximately
105 markets (0.3% of the total) which had 1,000 or more passengers a
day (i.e., thick
markets), but these accounted for almost 17% of the total passengers.
On the other hand,
there were almost 28,000 markets (78% of the total) with 10 or fewer
passengers a day that
accounted for only 6% of total passengers in 2003. These O&D market
pairs have been
served, generally speaking, by 52,000-56,000 flight segments (i.e.,
routings passengers took
to travel the markets) depending upon the extent and intensity of network.
In recent years,
however, the network segments have increased sizably to an average of
67,000-72,000
segments leading to increased fragmentation.
Understanding the evolutionary nature of the airline network is extremely
important.
Investment decisions with consequences stretching far into the future
are being made to
serve the airline network. A proper understanding of the dynamic nature
of the airline
network, therefore, is essential to minimize costly mistakes.
We used a multinomial logit model to analyze and determine itinerary
choices in the US
scheduled airline industry. Using 10% ticket sample data for the second
quarter of 2003, we
find that passengers, weighted average fare, average distance and types
of air carriers
empirically determine the itinerary choices. This simple model captures
lower-order
itinerary choices (i.e., those with less than three stops) fairly well
for the sample of almost
360,000 itineraries.
