An ATM Knowledge Graph-Based
Method of Dynamic Information Fusion
and Correlation Analysis
Qiucheng Xu
(B) , Yongjie Yan, Yungang Tian, Weiyu Jiang, and Jinglei Huang
State Key Laboratory of Air Traffic Management System and Technology,
The 28th Research Institute of China Electronics Technology Group Corporation,
Nanjing 210007, China
xuqiucheng1987@163.com
Abstract. In order to fuse information and analyze correlation more
efficiently and flexibly, an air traffic management (ATM) knowledge
graph-based method is proposed to reorganize the information flexibly
and manage the fusion process dynamically. After that, a breadth-first
and depth-first search-based correlation analysis method is designed to
find deeper correlations and improve the searching efficiency.
Keywords: Air traffic management · Knowledge graph · Information
fusion · Correlation analysis
1 Introduction
With the rapid development of air transport in China, the number of flights is
increasing rapidly at an average annual rate of over 10% in recent years [1]. It
is predicted that civil aviation transportation in China will maintain a growth
rate of about 12%. By 2030, there will be more than 450 civil transport airports,
more than 95% of county-level administrative regions and population in China
will receive air services, and the volume of passenger traffic will reach 1.8 billion
[2]. However, the rapid development has also brought more and more challenges
and pressures to the safe and efficient operation of air transport, for example,
flight delays have become more common than ever [3], and the aviation safety
accidents demonstrate a steadily rising tendency [4,5].
Air traffic management (ATM) has taken many measures to meet the challenges brought by rapid development of air traffic, such as building more surveillance and sensing equipment; developing a variety of information systems for
auxiliary control aiming at different stages and scenarios of air traffic control [6].
In addition, the air traffic control department has strengthened cooperation with
diverse departments, including airports, airlines, etc., to access more and more
kinds of information from different sources [7]. Above solutions can solve the
specific problems of air traffic controllers, while on the other hand, they could
also bring a series of new problems, such as the explosion of information scale,
difficulty of integration, and adding new workload to the controllers.
c
Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 487–496, 2020.
https://doi.org/10.1007/978-981-15-0187-6_58
Method of Dynamic Information Fusion
and Correlation Analysis
Qiucheng Xu
(B) , Yongjie Yan, Yungang Tian, Weiyu Jiang, and Jinglei Huang
State Key Laboratory of Air Traffic Management System and Technology,
The 28th Research Institute of China Electronics Technology Group Corporation,
Nanjing 210007, China
xuqiucheng1987@163.com
Abstract. In order to fuse information and analyze correlation more
efficiently and flexibly, an air traffic management (ATM) knowledge
graph-based method is proposed to reorganize the information flexibly
and manage the fusion process dynamically. After that, a breadth-first
and depth-first search-based correlation analysis method is designed to
find deeper correlations and improve the searching efficiency.
Keywords: Air traffic management · Knowledge graph · Information
fusion · Correlation analysis
1 Introduction
With the rapid development of air transport in China, the number of flights is
increasing rapidly at an average annual rate of over 10% in recent years [1]. It
is predicted that civil aviation transportation in China will maintain a growth
rate of about 12%. By 2030, there will be more than 450 civil transport airports,
more than 95% of county-level administrative regions and population in China
will receive air services, and the volume of passenger traffic will reach 1.8 billion
[2]. However, the rapid development has also brought more and more challenges
and pressures to the safe and efficient operation of air transport, for example,
flight delays have become more common than ever [3], and the aviation safety
accidents demonstrate a steadily rising tendency [4,5].
Air traffic management (ATM) has taken many measures to meet the challenges brought by rapid development of air traffic, such as building more surveillance and sensing equipment; developing a variety of information systems for
auxiliary control aiming at different stages and scenarios of air traffic control [6].
In addition, the air traffic control department has strengthened cooperation with
diverse departments, including airports, airlines, etc., to access more and more
kinds of information from different sources [7]. Above solutions can solve the
specific problems of air traffic controllers, while on the other hand, they could
also bring a series of new problems, such as the explosion of information scale,
difficulty of integration, and adding new workload to the controllers.
c
Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 487–496, 2020.
https://doi.org/10.1007/978-981-15-0187-6_58