Flight behavior analysis provides the fundamental basis for the future development of air traffic management(ATM).The characteristics of aircraft behavior are inherently reflected in their flight trajectories,impactin...Flight behavior analysis provides the fundamental basis for the future development of air traffic management(ATM).The characteristics of aircraft behavior are inherently reflected in their flight trajectories,impacting flight efficiency and safety levels.However,existing research largely addresses inefficient or abnormal trajectories from a single perspective,with an absence of a unified evaluation standard.This paper introduces a method for analyzing flight deviation behavior based on automatic dependent surveillance-broadcast(ADS-B)data,defining novel metrics of trajectory redundancy and trajectory deviation.An adaptive detection algorithm is developed to capture diverse deviation patterns.Results reveal that higher trajectory redundancy is linked to lower operational efficiency,while trajectory deviation effectively identify stepped descents,holding patterns,detours,and other behaviors.The approach offers data-driven support for anomaly detection,performance evaluation and air traffic management,with substantial significance for civil aviation applications.展开更多
基金supported in part by the National Key Research and Development Program of China(No.2023YFB4302903)the Fundamental Research Funds for the Central Universities(No.210525001464)。
文摘Flight behavior analysis provides the fundamental basis for the future development of air traffic management(ATM).The characteristics of aircraft behavior are inherently reflected in their flight trajectories,impacting flight efficiency and safety levels.However,existing research largely addresses inefficient or abnormal trajectories from a single perspective,with an absence of a unified evaluation standard.This paper introduces a method for analyzing flight deviation behavior based on automatic dependent surveillance-broadcast(ADS-B)data,defining novel metrics of trajectory redundancy and trajectory deviation.An adaptive detection algorithm is developed to capture diverse deviation patterns.Results reveal that higher trajectory redundancy is linked to lower operational efficiency,while trajectory deviation effectively identify stepped descents,holding patterns,detours,and other behaviors.The approach offers data-driven support for anomaly detection,performance evaluation and air traffic management,with substantial significance for civil aviation applications.
