MTME-transformer for AIS-based vessel trajectory prediction

Abstract

Vessel trajectory prediction is a key basis for port traffic monitoring, collision-risk identification and navigation decision support. However, AIS data are often affected by irregular sampling, noise and complex manoeuvring behaviours in port waters, making it difficult for models to simultaneously capture global navigation trends and local motion variations. To address this issue, this study proposes a Transformer-based trajectory prediction model enhanced by multi-scale temporal motion encoding, termed MTME-Transformer. The model introduces temporal convolutional branches with different kernel sizes into the Transformer encoder to capture motion patterns over short, medium and wider temporal receptive fields, and is evaluated under a unified data preprocessing, resampling and multi-step autoregressive prediction framework. Experimental results show that, under a 2-min sampling interval and a 30-min prediction horizon, MTME outperforms the Transformer and RNN-based comparison models across multiple evaluation metrics. Ablation experiments further indicate that larger kernel scales are more important for trajectory extrapolation. These results suggest that multi-scale local motion modelling can stably improve the accuracy of AIS-based vessel trajectory prediction.