Tensor completion-based trajectory imputation approach in air traffic control

Abstract

The flight trajectory in air traffic control systems usually misses some updating positions because of unexpected errors. In this paper, a tensor completion-based approach is proposed to recover missing positions from a whole trajectory dataset. Considering the trajectory dependencies among different operations, the flight trajectories with the same flight number are organized as a three-dimensional tensor. A trace norm minimizing based tensor completion method is performed on the trajectory tensor to achieve the imputation task, in which the Block Coordinate Descent algorithm is applied to optimize the tensor model. Unlike other data-driven algorithms, the proposed approach captures the global information (route similarity and transition patterns) from the whole tensor, which is further applied to estimate the missing values in a training-free manner. Several experiments are designed to validate the proposed approach, including the padding methods, the dataset size, and the imputation performance on different missing patterns and rates. Experimental results on real-world flight trajectories show that the proposed approach can (1) estimate missing positions with high accuracy even on a small dataset, (2) recover missing positions even if the random missing rate up to 90%, (3) overcome the situation of the flight chain missing and block missing, which are the barriers of existing methods. The proposed approach serves as a post-processing procedure of air traffic data and can further provide high-quality data to other air traffic studies.