Abstract
Timely and efficient air traffic flow management (ATFM) is a key issue
in future dense air traffic. The emerging demands for unmanned aerial
vehicles and general aviation aircraft aggravate the burden of the ATFM.
Thanks to the advanced automatic dependent surveillance-broadcast
(ADS-B) technique, the aerial vehicles can be tracked and monitored in a
real-time and accurate manner, providing possibility for establishing a
more intelligent ATFM architecture. In this paper, we first form an
aviation big data platform by using the distributed ADS-B ground
stations and the obtained ADS-B messages. By exploring the constructed
dataset and mapping the extracted information to the routes, the air
traffic flow between different cities can be counted and predicted,
where the prediction task is implemented on the basis of two machine
learning methods, respectively. The experimental results based on
real-world data demonstrate that the proposed traffic flow prediction
model adopting long short-term memory (LSTM) can achieve better
performance, especially when abnormal factors in traffic control are
considered.