  The volume of flight traffic gets increasing over the time, which makes the
strategic traffic flow management become one of the challenging problems since
it requires a lot of computational resources to model entire traffic data. On
the other hand, Automatic Dependent Surveillance - Broadcast (ADS-B) technology
has been considered as a promising data technology to provide both flight crews
and ground control staff the necessary information safely and efficiently about
the position and velocity of the airplanes in a specific area. In the attempt
to tackle this problem, we presented in this paper a simplified framework that
can support to detect the typical air routes between airports based on ADS-B
data. Specifically, the flight traffic will be classified into major groups
based on similarity measures, which helps to reduce the number of flight paths
between airports. As a matter of fact, our framework can be taken into account
to reduce practically the computational cost for air flow optimization and
evaluate the operational performance. Finally, in order to illustrate the
potential applications of our proposed framework, an experiment was performed
using ADS-B traffic flight data of three different pairs of airports. The
detected typical routes between each couple of airports show promising results
by virtue of combining two indices for measuring the clustering performance and
incorporating human judgment into the visual inspection.
