Trajectory clustering can identify the flight patterns of the air traffic,which in turn contributes to the airspace planning,air traffic flow management,and flight time estimation.This paper presents a semantic-based ...Trajectory clustering can identify the flight patterns of the air traffic,which in turn contributes to the airspace planning,air traffic flow management,and flight time estimation.This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation.The proposed method consists of four significant steps:representing the trajectories,grouping the trajectories based on the new representation,measuring the similarities between different trajectories through dynamic time warping(DTW)in each group,and clustering the trajectories based on k-means and densitybased spatial clustering of applications with noise(DBSCAN).We take the inbound trajectories toward Shanghai Pudong International Airport(ZSPD)to carry out the case studies.The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns,but also improve the performance of flight time estimation.展开更多
With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Curren...With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.展开更多
基金supported by the Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China(U1933117)the Open Fund for Graduate Innovation Base(Laboratory)of Nanjing University of Aeronautics and Astronautics(kfjj20190709).
文摘Trajectory clustering can identify the flight patterns of the air traffic,which in turn contributes to the airspace planning,air traffic flow management,and flight time estimation.This paper presents a semantic-based trajectory clustering method for arrival aircraft via new proposed trajectory representation.The proposed method consists of four significant steps:representing the trajectories,grouping the trajectories based on the new representation,measuring the similarities between different trajectories through dynamic time warping(DTW)in each group,and clustering the trajectories based on k-means and densitybased spatial clustering of applications with noise(DBSCAN).We take the inbound trajectories toward Shanghai Pudong International Airport(ZSPD)to carry out the case studies.The corresponding results indicate that the proposed method could not only distinguish the particular flight patterns,but also improve the performance of flight time estimation.
基金supported by the National Natural Science Foundation of China(Grant No.61772249).
文摘With the rapid development of Global Positioning System(GPS),Global System for Mobile Communications(GSM),and the widespread application of mobile devices,a massive amount of trajectory data have been generated.Current trajectory data processing methods typically require input in the form of fixed-length vectors,making it crucial to convert variable-length trajectory data into fixed-length,low-dimensional embedding vectors.Trajectory representation learning aims to transform trajectory data into more expressive and interpretable representations.This paper provides a comprehensive review of the research progress,methodologies,and applications of trajectory representation learning.First,it categorizes and introduces the key techniques of trajectory representation learning and summarizes the available public trajectory datasets.Then,it classifies trajectory representation learning methods based on various downstream tasks,with a focus on their principles,advantages,limitations,and application scenarios in trajectory similarity computation,similar trajectory search,trajectory clustering,and trajectory prediction.Additionally,representative model structures and principles in each task are analyzed,along with the characteristics and advantages of different methods in each task.Last,the challenges faced by current trajectory representation learning methods are analyzed,including data sparsity,multimodality,model optimization,and privacy protection,while potential research directions and methodologies to address these challenges are explored.
