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.

翻译：海上船舶活动建模对于航线规划、运输物流、海事安全与环境监测等广泛领域至关重要。过去二十年间，自动识别系统（AIS）实现了对数十万艘船舶的实时监控，每日产生海量数据。利用AIS数据面临的主要挑战在于船舶轨迹中存在大量缺失段，这通常由信号覆盖限制或人为传输中断导致。此类数据缺口会显著降低数据质量，导致分析结果不准确或不完整。现有的先进补全方法主要针对车辆轨迹设计，即使未考虑底层道路网络。但船舶航行模式存在本质差异，例如平滑转向、港口附近机动航行或恶劣天气条件下的导航。在本应用研究中，我们提出HABIT——一种轻量级、可配置的基于H3聚合的船舶轨迹补全框架。该数据驱动框架通过从历史AIS数据中提取、分析并索引运动模式，为缺失轨迹段补全提供了有效手段。我们在不同时间跨度、数据密度及船舶类型的AIS数据集上的实证研究表明：在考虑船舶特性及其运动模式的前提下，HABIT生成的海事轨迹补全结果在精度方面与基线方法相当，而在延迟性能方面表现更优。