Generation of Optimized Trajectories for Congestion Mitigation in Fukuoka Approach Control Area Using Deep Reinforcement Learning

被引:0
作者
Iwatsuki, Yota [1 ]
Kawamoto, Yasutaka [1 ,2 ]
Higashino, Shin-Ichiro [2 ]
机构
[1] Kyushu Univ, Dept Aeronaut & Astronaut, Fukuoka, Japan
[2] Japan Air Lines, Flight Crew Dept B787, Tokyo, Japan
来源
2023 ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY, VOL I, APISAT 2023 | 2024年 / 1050卷
关键词
Air traffic management; Fukuoka approach control area; Reduce radar vector; Deep reinforcement learning;
D O I
10.1007/978-981-97-3998-1_89
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Fukuoka airport is the busiest single runway airport in Japan and the congestion has been increasing year by year. The COVID-19 pandemic has temporarily eased the congestion, but it is expected to increase again after the pandemic. Excessive radar vector by air traffic controllers to maintain aircraft separation during congestion causes economic and environmental losses due to increased flight distances and times. We attempted to generate optimal trajectories in the approach control area of Fukuoka airport using a deep reinforcement learning method based on a centralized Deep Q Network (DQN). Wind information was considered in the trajectory optimization using Mesoscale Model (MSM) data from the Japan Meteorological Agency. As a result, the optimized trajectory was found to be useful because a radar vector reduction of 23.7% in distance and 33.6% in flight time were attained compared with the recorded radar data provided as CARATS open data. The trajectories have the characteristics that all of the Ground speed (GS) of aircraft are made slower while directed straight to the intermediate fix (IF) after entering the approach control area.
引用
收藏
页码:1087 / 1113
页数:27
相关论文
共 32 条
[1]  
Aeronautical Information Service Center, OSTEP ARRIVAL/ISKUP ARRIVAL/ ATSAG ARRIVAL/ SARUP ARRIVA
[2]   Rule derivation for arrival aircraft sequencing [J].
Andreeva-Mori, Adriana ;
Suzuki, Shinji ;
Itoh, Eri .
AEROSPACE SCIENCE AND TECHNOLOGY, 2013, 30 (01) :200-209
[3]  
Civil Aviation Bureau Ministry of Land Infrastructure Transport and Tourism, 2014, Fukuokakuukou kassouro zousetsuzigyou ni okeru shinkizigyou saitakuzi no hyouka ni tusite
[4]  
Cornish Hellaby Watkins ChristopherJohn., 1989, LEARNING DELAYED REW
[5]  
Dalmau R., 2020, 10 SESAR INN DAYS SI
[6]  
Diederik P.K., 2015, 3 INT C LEARN REPR S
[7]  
Erzberger H., 2005, 25 INT C AER SCI
[8]  
Eurocontrol, Base of Aircraft Data (BADA)
[9]  
Ghosh S, 2021, Proceedings of the International Conference on Automated Planning and Scheduling, V31, P468, DOI [10.1609/icaps.v31i1.15993, 10.1609/icaps.v31i1.15993, DOI 10.1609/ICAPS.V31I1.15993]
[10]  
IATA, 2022, Tourism Economics Air Passenger Forecast