Impact of temporal resampling interval on deep learning-based vessel trajectory prediction using AIS data
Year: 2026
Type: article
Abstract: This study examines how temporal resampling intervals affect deep learning-based vessel trajectory prediction using Automatic Identification System (AIS) data. For this purpose, a public AIS dataset from the National Oceanic and Atmospheric Administration (NOAA) was filtered, validated, and transformed into trajectory datasets with four different time intervals: 1 min, 5 min, 10 min, and 20 min. Six learning-based models were tested under the same experimental conditions and one-step-ahead prediction setting. The evaluated models were Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (BiLSTM), Transformer, Transformer-LSTM, and Transformer-GRU. A constant speed-course baseline was also used as a reference method. The results show that prediction error increases as the resampling interval becomes longer. Shorter intervals provide more detailed information about vessel motion and improve continuity between consecutive trajectory points. This leads to lower training loss, Average Displacement Error (ADE), and Root Mean Square Error (RMSE) values. Transformer-GRU showed the strongest performance at the 20 min interval. In contrast, standalone recurrent models became more sensitive when the temporal resolution became coarser. The baseline method also remained competitive for relatively smooth and approximately linear vessel motion. These results indicate that temporal resampling should not be regarded solely as a preprocessing step, as the resulting temporal configuration influences model behavior, prediction error, and comparative performance. Although the proposed analysis was validated using AIS datasets collected on three representative dates, future studies should investigate a broader range of temporal and operational conditions to further assess the generalizability of the findings.
Authors: Sercan Külcü, Adil Kondiloğlu
Institution: Giresun University
Language: Turkish
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7224000716