• 한국항공운항학회지
• /
• 제26권2호
• /
• pp.39-46
• /
• 2018

Departure Manager (DMAN) is a tool to optimize the departure sequence and to suggest appropriate take-off time and off-block time of each departure aircraft to the air traffic controllers. To that end, Variable Taxi Time (VTT), which is time duration of the aircraft from the stand to the runway, should be estimated. In this paper, a study for development of VTT prediction algorithm based on machine learning techniques is presented. The factors affecting aircraft taxi speeds were identified through the analysis of historical traffic data on the airport surface. The prediction model suggested in this study consists of several sub-models that reflect different types of surface maneuvers based on the analysis result. The prediction performance of the proposed method was evaluated using the actual operational data.

• 한국항공운항학회:학술대회논문집
• /
• 한국항공운항학회 2015년도 추계학술대회
• /
• pp.163-166
• /
• 2015

본 연구는 공항과 항공기 운영 효율의 향상을 위해 도입되고 있는 A-CDM의 주요구성요소인 Variable Taxi Time을 계산하기 위해 필요한 요건들과 유사 사례들을 검토하고, 특히 A-SMGCS와 같은 Surface Manager 기능에서 항공기의 지상이동 과정의 Taxi Time 계산에 영향을 미치는 다양한 요소와 동적요인을 분석하여 이를 토대로 VTT를 계산하는 접근방법을 소개하고자 한다.