ACTIVE: Continuous Similarity Search for Vessel Trajectories

2025-04-01

AI Summary

Existing ship trajectory similarity search methods struggle to handle real-time AIS streaming data. To address this, this paper proposes a continuous similarity search framework tailored for real-time trajectory streams. Its core contributions are threefold: (1) introducing the “object-trajectory real-time distance” metric to dynamically quantify similarity based on predicted future motion trends; (2) designing a lightweight, trajectory-segment-based indexing structure enabling efficient incremental updates; and (3) developing the Continuous Similar Trajectory Search (CSTS) algorithm, which integrates multi-level search-space pruning with incremental matching. Experiments demonstrate that, compared to state-of-the-art methods, CSTS reduces query latency by 70%, improves similarity hit rate by 60%, and significantly lowers index construction overhead and storage footprint. The framework thus effectively supports safety-critical, latency-sensitive maritime applications such as collision avoidance and intelligent navigation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Publicly available vessel trajectory data is emitted continuously from the global AIS system. Continuous trajectory similarity search on this data has applications in, e.g., maritime navigation and safety. Existing proposals typically assume an offline setting and focus on finding similarities between complete trajectories. Such proposals are less effective when applied to online scenarios, where similarity comparisons must be performed continuously as new trajectory data arrives and trajectories evolve. We therefore propose a real-time continuous trajectory similarity search method for vessels (ACTIVE). We introduce a novel similarity measure, object-trajectory real-time distance, that emphasizes the anticipated future movement trends of vessels, enabling more predictive and forward-looking comparisons. Next, we propose a segment-based vessel trajectory index structure that organizes historical trajectories into smaller and manageable segments, facilitating accelerated similarity computations. Leveraging this index, we propose an efficient continuous similar trajectory search (CSTS) algorithm together with a variety of search space pruning strategies that reduce unnecessary computations during the continuous similarity search, thereby further improving efficiency. Extensive experiments on two large real-world AIS datasets offer evidence that ACTIVE is capable of outperforming state-of-the-art methods considerably. ACTIVE significantly reduces index construction costs and index size while achieving a 70% reduction in terms of query time and a 60% increase in terms of hit rate.

Problem

• Real-time continuous vessel trajectory similarity search
• Predictive similarity measure for future movement trends
• Efficient indexing and pruning for online trajectory comparisons

Innovation

• Real-time continuous trajectory similarity search method
• Object-trajectory real-time distance measure
• Segment-based vessel trajectory index structure