ACTIVE: Continuous Similarity Search for Vessel Trajectories

TL;DR

This work tackles real-time continuous vessel trajectory similarity search using AIS data. It introduces the Object-Trajectory Real-time Distance (OTRD), combining Historical Trajectory Distance and Target-Trajectory Distance to incorporate past movement, future trends, and destination information, and integrates it into the CSTS algorithm operating on a segment-based SVTI index. Through four speed-up strategies and an incremental computation framework, ACTIVE achieves significant improvements in query time and hit rate while reducing index construction costs, demonstrated on two large AIS datasets. The framework enables proactive maritime decision-making for collision avoidance, route optimization, and traffic management, marking a step toward scalable, real-time trajectory analytics in open waters.

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 an efficient continuous similar trajectory search (CSTS) algorithm together with a segment-based vessel trajectory index and 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.

Figures (18)

• Figure 1: Example of trajectories at timestamp $t$.
• Figure 2: ACTIVE framework.
• Figure 3: Examples of gaps between existing similarity measures and real needs.
• Figure 4: Example of OTRD measure.
• Figure 5: An example of SVTI.

Theorems & Definitions (3)

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