A reliable early warning framework for vessel-bridge collisions via spatiotemporal trajectory prediction and anomaly detection

Abstract

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.