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A stacking-based ensemble learning model for intelligent ship trajectory interpolation
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DOI：10.1016/j.ress.2025.111615.png)
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Abstract
En 中文
Incomplete ship trajectories caused by irregular Automatic Identification System (AIS) updates pose a critical challenge to reliable modeling of ship behaviors, which underpins the understanding and management of complex maritime traffic systems. Ship trajectory interpolation has therefore become essential for reconstructing missing segments and ensuring data continuity. However, most existing methods adopt an individual interpolation model regardless of varying ship behaviors, which limits their adaptability and may degrade the accuracy of reconstructed trajectories in diverse traffic scenarios. This study presents a Stacking Trajectory Interpolation Model (STIM) that enables adaptive and behavior-aware selection of the most suitable interpolation algorithm for accurate and robust ship trajectory data reconstruction. Specifically, a Markov-based feature extraction approach is first designed to divide trajectories into segments reflective of behavioral patterns, providing informative inputs to support the model’s learning process. Five widely adopted interpolation methods (linear, polynomial, cubic spline, cubic Hermite, and kinematic interpolation) are then implemented in base learners for initial predictions. The meta-learner empowered by a Transformer-based dual-branch multi-classifier subsequently learns the latent relationship between segment features and interpolation performance, enabling the model to support reliable trajectory reconstruction under diverse behavioral patterns. Experimental results using AIS data from Ningbo-Zhoushan Port demonstrate that STIM has strong adaptability and scalability in handling diverse trajectory characteristics, generating more accurate interpolated trajectories compared to conventional methods. Additionally, the impact of different trajectory features on interpolation results is discussed, which further validates the strength of STIM in providing valuable insights for optimizing future trajectory-based application solutions.
Keywords:
Ship trajectory interpolation
Ensemble learning
Stacking model
Transformer
Feature extraction
Maritime traffic engineering
AIS data
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