Aug 2026· International Conference on Electromechanical Control Technology and Transportation· Vol 14324, pp. 1432423 - 1432423-7· 0 citations· 10 references
Engineering
TL;DR
It is suggested that multi-scale local motion modelling can stably improve the accuracy of AIS-based vessel trajectory prediction and is evaluated under a unified data preprocessing, resampling and multi-step autoregressive prediction framework.
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.
These findings demonstrate that H3-indexed context, structured at multiple geographic resolutions and integrated through a selective mechanism, serves as transferable spatial context for vessel trajectory prediction.
Accurate short-term vessel trajectory prediction is important for traffic monitoring and collision-risk screening in port-approach waters, where vessels frequently turn, accelerate, decelerate, and merge into traffic lanes. This study develops a maneuver-aware residual multi-scale long short-term memory (LSTM) framework for Automatic Identification System (AIS)-based 10–30 min trajectory prediction. The method predicts residual displacement relative to the last observed position, constructs maneuver-aware features from local displacement, course variation, speed variation, turning rate, and acceleration-like terms, and compares fixed and maneuver-guided multi-scale fusion strategies. Experiments are conducted on public AIS data from San Francisco Bay and adjacent approach waters using Maritime Mobile Service Identity (MMSI)-level train/validation/test splits and three random seeds. The largest observed gains come from residual prediction and maneuver-aware features. In the three-seed main evaluation, the fixed multi-scale LSTM (Fixed-MS-LSTM) provides the strongest 10 min accuracy, while the maneuver-guided multi-scale residual LSTM (MGMS-RLSTM) achieves lower average displacement error (ADE) at 20 and 30 min and learns distinct temporal-scale preferences across straight, turning, and speed-changing samples. Encounter-oriented closest point of approach (CPA) and time to closest point of approach (TCPA) evaluation further shows that the residual multi-scale models support more accurate CPA/TCPA-based high-risk screening under the evaluated benchmark. These findings indicate that maneuver-guided fusion can be characterized as a horizon-dependent scale-selection mechanism that complements the fixed multi-scale counterpart.
Xinyue Lin, Xiao-Han Zhang, Wendong Bao et al.· Journal of Marine Science an...· 0 citations
Accurate vessel trajectory prediction is critical for maritime safety and anomaly detection, yet existing models often struggle with geographic bias and navigational realism. We propose the Continuous Regression Hybrid Transformer (CRHT), a deep learning framework designed to forecast vessel motion using Automatic Identification System (AIS) data. To mitigate spatial data imbalance, we introduce an online K-means cluster sampling strategy that ensures diverse exposure to rare maneuvers during training. Our hybrid architecture integrates 1D convolutional layers for local kinematic feature extraction with a multi-head attention mechanism for global temporal context. CRHT demonstrates superior performance in short-term forecasting, achieving the lowest errors at the 1-hour horizon. The results demonstrate that while discrete models provide high navigational stability over long horizons, CRHT offers an optimal balance of precision and maneuver tracking for real-time maritime surveillance.
Alexander Schiøtz, Bertram Hage, Christian Rand et al.· 0 citations
With the growing scale and number of vessels, inland waterway traffic environments have become more complex, especially in bridge waterways where vessel-bridge collisions occur frequently. To enhance navigational safety, this paper proposes a reliable early warning framework based on spatiotemporal trajectory prediction and anomaly detection. We utilize Automatic Identification System (AIS) data and construct a trajectory prediction model that integrates a Multi-Head Attention mechanism with a Long Short-Term Memory (LSTM) network. A collaborative optimization strategy is adopted to fine-tune the model’s hyperparameters, significantly enhancing its performance on complex spatiotemporal sequences. To address the challenge of identifying abnormal vessel trajectories, we design an enhanced autoencoder network that couples spatial encoding with dynamic behavior modeling to effectively extract latent trajectory features. By incorporating Dynamic Time Warping (DTW) and time series clustering, the model further enables unsupervised anomaly detection and classification. Furthermore, typical abnormal navigation patterns in bridge waterways are simulated using the full-mission ship maneuvering simulator, generating high-quality abnormal trajectory data to improve the model’s generalization and early warning capability. Experimental results demonstrate that the proposed method achieves excellent performance in both trajectory prediction and anomaly detection. It offers a reliable and practical solution for intelligent vessel navigation and proactive risk warning in complex inland bridge environments.
Jing-Xin Cao, Yuan-Zhou Zheng, Long Qian et al.· Scientific Reports· 0 citations
Accurate long-term trajectory prediction for maritime vessels is essential for safety and logistical efficiency. While deep learning models, particularly Transformers, have shown promise in processing Automatic Identification System (AIS) data, they often struggle with the quadratic computational complexity of self-attention and the loss of accuracy over extended forecasting horizons. This study proposes TempTPI, a novel prediction framework that integrates an Informer-based encoder with a multi-channel temporal encoding mechanism. The Informer architecture leverages a ProbSparse self-attention mechanism to reduce computational overhead and focus on the most significant dependencies, while the temporal encoder utilizes Fourier-like frequency expansions to capture cyclic patterns (hourly, daily, and seasonal) in vessel behavior. We evaluate our model against the state-of-the-art TPTrans architecture using AIS data from Danish waters. Experimental results demonstrate that TempTPI consistently outperforms existing methods across prediction windows of 1 to 5 hours. Notably, at a 5-hour horizon, the proposed model achieves a 55% improvement in Mean Squared Error (MSE), offering a robust solution for long-range maritime situational awareness.
Kevin Ferneding, Veronika Lietavcova, Aleksandra M. Blachowiak et al.· 0 citations
Vessel trajectory prediction is critical for maritime safety and accident prevention. While most existing trajectory prediction models rely on Automatic Identification System (AIS) data due to its precision and availability, small vessels mostly operate without AIS, resulting in a significant monitoring gap. To address this, we propose Mix&Fix-Net, a dual-stage mixer-based trajectory prediction model designed to handle vessel trajectory time-series data derived from both AIS and (non-AIS) vision data. Our architecture integrates a Primary Trajectory Predictor with a Residual Trajectory Adjuster, enabling more refined trajectory prediction. Additionally, we introduce a new video-based dataset derived from webcam streams, from which vessel trajectories are extracted to represent non-AIS data. Extensive evaluations on both AIS and non-AIS datasets across six metrics (mean squared error, mean absolute error, symmetric mean absolute percentage error, final displacement error, Frechet distance, and average Euclidean distance) demonstrate that Mix&Fix-Net consistently outperforms existing baselines across most metrics and datasets.
M. Murad, Bora San Turgut, Yasin Yilmaz· Ocean Engineering· 0 citations
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