Data-Driven Trajectory Imputation for Vessel Mobility Analysis

2026-02-12

AI Summary

This study addresses the challenge of large gaps in vessel AIS trajectories caused by signal loss. To this end, the authors propose HABIT, a lightweight and configurable framework that integrates H3 geospatial indexing with vessel-specific motion patterns to enable efficient and accurate trajectory imputation. By leveraging historical trajectory clustering and data-driven modeling, HABIT identifies航行 behaviors similar to the target vessel and reconstructs missing segments accordingly. Extensive experiments on real-world AIS datasets across diverse vessel types and varying spatiotemporal densities demonstrate that HABIT achieves accuracy comparable to state-of-the-art methods while significantly reducing computational latency. The framework thus provides high-temporal-resolution and high-completeness trajectory data, supporting timely and reliable maritime mobility analysis.

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: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modeling vessel activity at sea is critical for a wide range of applications, including route planning, transportation logistics, maritime safety, and environmental monitoring. Over the past two decades, the Automatic Identification System (AIS) has enabled real-time monitoring of hundreds of thousands of vessels, generating huge amounts of data daily. One major challenge in using AIS data is the presence of large gaps in vessel trajectories, often caused by coverage limitations or intentional transmission interruptions. These gaps can significantly degrade data quality, resulting in inaccurate or incomplete analysis. State-of-the-art imputation approaches have mainly been devised to tackle gaps in vehicle trajectories, even when the underlying road network is not considered. But the motion patterns of sailing vessels differ substantially, e.g., smooth turns, maneuvering near ports, or navigating in adverse weather conditions. In this application paper, we propose HABIT, a lightweight, configurable H3 Aggregation-Based Imputation framework for vessel Trajectories. This data-driven framework provides a valuable means to impute missing trajectory segments by extracting, analyzing, and indexing motion patterns from historical AIS data. Our empirical study over AIS data across various timeframes, densities, and vessel types reveals that HABIT produces maritime trajectory imputations performing comparably to baseline methods in terms of accuracy, while performing better in terms of latency while accounting for vessel characteristics and their motion patterns.

Problem

• trajectory imputation
• AIS data
• vessel mobility
• missing data
• maritime trajectories

Innovation

• trajectory imputation
• vessel mobility
• AIS data
• H3 aggregation
• maritime motion patterns