An Ontology for Decision-Making
Support in Air Traffic Management
Yin Sheng
1(B) , Xi Chen
1 , Haijian Mo
1 , Xin Chen
2 , and Yang Zhang
1
1 State Key Laboratory of Air Traffic Management System and Technology, 1
Yongzhi Road, Qinhuai District, Nanjing City 210000, Jiangsu Province, China
shengyin1218@qq.com, chenxizhongqiu@126.com, aiwin28@163.com,
nwpu zhangy@163.com
2 Nanjing University of Finance and Economics, 3 Wenyuan Road, Qixia District,
Nanjing City 210000, Jiangsu Province, China
msxinchen@nufe.edu.cn
Abstract. Decision-making processes of Air Traffic Management
(ATM) need to organize information from different data sources. For
example, reroute requires four-dimensional (4D) trajectory and weather
information. Future ATM Decision-Support Systems (DSS) are expected
to perform trustworthy complex decision-making automatically. This
paper proposes an approach to DSS in ATM based on an ontology including concepts and instances of trajectories and meteorology. Temporal and
spatial relationships are then set in this ontology. Reasoning rules are also
build to represent knowledge in DSS. A case study shows a reroute scenario during a thunderstorm. In this scenario, information of the flight
and the weather is combined to support decision-making by the proposed
approach.
Keywords: Ontology · Air Traffic Management · Decision-Support
System
1 Introduction
Systems in Air Traffic Management (ATM) are complex. Various data sources,
e.g., weather information, flight plans, and airport information, must be organized to support the decision-making in the ATM. Crew members and air traffic
controllers must process complex information to guarantee the safety of flights.
Utilizing data from different sources in a consistent way is challenging for
enterprises, e.g., governments, airlines, or information service providers. Usually,
This work is supported by National Key R&D Program of China (No.
2016YFB0502405) and The Youth Program of Humanities and Social Sciences Foundation of the Ministry of Education of China (No. 18YJC630013).
c
Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 458–466, 2020.
https://doi.org/10.1007/978-981-15-0187-6_55
Support in Air Traffic Management
Yin Sheng
1(B) , Xi Chen
1 , Haijian Mo
1 , Xin Chen
2 , and Yang Zhang
1
1 State Key Laboratory of Air Traffic Management System and Technology, 1
Yongzhi Road, Qinhuai District, Nanjing City 210000, Jiangsu Province, China
shengyin1218@qq.com, chenxizhongqiu@126.com, aiwin28@163.com,
nwpu zhangy@163.com
2 Nanjing University of Finance and Economics, 3 Wenyuan Road, Qixia District,
Nanjing City 210000, Jiangsu Province, China
msxinchen@nufe.edu.cn
Abstract. Decision-making processes of Air Traffic Management
(ATM) need to organize information from different data sources. For
example, reroute requires four-dimensional (4D) trajectory and weather
information. Future ATM Decision-Support Systems (DSS) are expected
to perform trustworthy complex decision-making automatically. This
paper proposes an approach to DSS in ATM based on an ontology including concepts and instances of trajectories and meteorology. Temporal and
spatial relationships are then set in this ontology. Reasoning rules are also
build to represent knowledge in DSS. A case study shows a reroute scenario during a thunderstorm. In this scenario, information of the flight
and the weather is combined to support decision-making by the proposed
approach.
Keywords: Ontology · Air Traffic Management · Decision-Support
System
1 Introduction
Systems in Air Traffic Management (ATM) are complex. Various data sources,
e.g., weather information, flight plans, and airport information, must be organized to support the decision-making in the ATM. Crew members and air traffic
controllers must process complex information to guarantee the safety of flights.
Utilizing data from different sources in a consistent way is challenging for
enterprises, e.g., governments, airlines, or information service providers. Usually,
This work is supported by National Key R&D Program of China (No.
2016YFB0502405) and The Youth Program of Humanities and Social Sciences Foundation of the Ministry of Education of China (No. 18YJC630013).
c
Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 458–466, 2020.
https://doi.org/10.1007/978-981-15-0187-6_55