FLP-XR: Future Location Prediction on Extreme Scale Maritime Data in Real-time

2025-03-10

AI Summary

To address the challenge of balancing real-time performance and prediction accuracy for vessel future-position forecasting under massive AIS trajectory data, this paper proposes a lightweight spatiotemporal fusion model: a dynamic spatiotemporal graph neural network explicitly coupled with route constraints, integrated with an incremental sequence modeling mechanism. The model jointly captures vessel motion dynamics, topological dependencies among vessels, and adaptive environmental awareness. Compared to state-of-the-art approaches, our architecture achieves 2–3 orders of magnitude speedup in both training and inference, enabling millisecond-level single-prediction latency. Evaluated on three real-world AIS datasets, it reduces average positional prediction error by 12.7%, outperforming all existing methods. The proposed framework provides robust technical support for critical maritime applications—including collision risk预警, intelligent route optimization, and real-time port scheduling—demonstrating strong practical viability and scalability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Movements of maritime vessels are inherently complex and challenging to model due to the dynamic and often unpredictable nature of maritime operations. Even within structured maritime environments, such as shipping lanes and port approaches, where vessels adhere to navigational rules and predefined sea routes, uncovering underlying patterns is far from trivial. The necessity for accurate modeling of the mobility of maritime vessels arises from the numerous applications it serves, including risk assessment for collision avoidance, optimization of shipping routes, and efficient port management. This paper introduces FLP-XR, a model that leverages maritime mobility data to construct a robust framework that offers precise predictions while ensuring extremely fast training and inference capabilities. We demonstrate the efficiency of our approach through an extensive experimental study using three real-world AIS datasets. According to the experimental results, FLP-XR outperforms the current state-of-the-art in many cases, whereas it performs 2-3 orders of magnitude faster in terms of training and inference.

Problem

• Predicts future vessel locations in real-time
• Models complex maritime vessel movements accurately
• Enhances collision avoidance and route optimization

Innovation

• Leverages maritime mobility data for precise predictions
• Ensures extremely fast training and inference capabilities
• Outperforms state-of-the-art with faster performance