Approach and landing accidents (ALAs) have resulted in a significant number of hull losses worldwide , aside from runway excursion, hard landing, landing short . Technologies (e.g., instrument landing system) and procedures (e.g., stabilized approach criteria) have been developed to reduce ALA risks. In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of landing dynamicsfor aircrew and air traffic controller (ATCO) in real-time . Specifically, we develop a tunnel Gaussian process (TGP) model to gain an insight into the landing dynamics of aircraft using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight. TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates. We examine TGP qualitatively and quantitatively by synthesizing two complex trajectory datasets  . Empirically, TGP reconstructed the structure of the synthesized trajectories . When applied to operational A-SMGCS data, TGP provides the  probabilistic description of landing dynamics and interpretable 4D tunnel views of approach and landing parameters. The 4D tunnel views can facilitate the analysis of procedure adherence and augment existing aircrew and ATCO' s display during the approach and landing procedures, enabling necessary corrective actions. The proposed TGP model can also provide insights and aid the design of landing procedures in complex runway configurations such as parallel approach. Moreover, the extension of TGP model to the next generation of landing systems (e.g., GNSS landing system) is straight-forward. The interactive visualization of our findings are available at URL
Approach and landing accidents have resulted in a significant number of hull losses worldwide . Technologies (e.g., instrument landing system) and procedures (e.g., stabilized approach criteria) have been developed to reduce the risks. In this paper, we propose a data-driven method to learn and interpret flight's 4D approach and landing parameters to facilitate comprehensible and actionable insights of flight dynamics . Specifically, we develop two variants of tunnel Gaussian process (TGP) models to elucidate aircraft's approach and landing dynamics using advanced surface movement guidance and control system (A-SMGCS) data, which then indicates the stability of flight. TGP hybridizes the strengths of sparse variational Gaussian process and polar Gaussian process to learn from a large amount of data in cylindrical coordinates. We examine TGP qualitatively and quantitatively by synthesizing three complex trajectory datasets and compared TGP against existing methods on trajectory learning . Empirically, TGP demonstrates superior modeling performance . When applied to operational A-SMGCS data, TGP provides the generative probabilistic description of landing dynamics and interpretable 4D tunnel views of approach and landing parameters. These 4D tunnel models can facilitate the analysis of procedure adherence and augment existing aircrew and air traffic controllers' display during the approach and landing procedures, enabling necessary corrective actions. 
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The proposed TGP model can also provide insights and aid the design of landing procedures in complex runway configurations such as parallel approach. Moreover, the extension of TGP model to the next generation of landing systems (e.g., GNSS landing system) is straight-forward. The interactive visualization of our findings are available at URL