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 article, 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.
Highlights
D UE to the unprecedented development of the civil aviation industry, efficient and comfortable air travel has become the priority choose for more and more passengers
The rising demand for air travel and the accompanying rapid growth of flights will inevitably lead to the air traffic congestion in limited air space, which will burden the work of air traffic management (ATM), and pose formidable challenges to air traffic surveillance systems
We propose two prediction models based on the support vector regression (SVR) and long shortterm memory (LSTM), respectively, which are trained by the massive automatic dependent surveillance-broadcast (ADS-B) data obtained from our aviation big data platform
Summary
D UE to the unprecedented development of the civil aviation industry, efficient and comfortable air travel has become the priority choose for more and more passengers. This paper explores the researches on statistics and prediction of the air route traffic flow based on big volume real ADS-B message. The flow prediction task can be implemented based on two machine learning methods: support vector regression (SVR) [24] and long short-term memory (LSTM) [25]. We find that both the SVR and LSTMbased prediction models can adequately predict the air route flow, and better performance can be obtained by the LSTMbased model using big volume dataset. The geometrical model for air traffic flow statistics is constructed, which can be used for statistic and visualization
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