标题： 主动：船舶航迹的连续相似性搜索 显示英文标题

标题： ACTIVE: Continuous Similarity Search for Vessel Trajectories

摘要： 公开可用的船舶轨迹数据持续从全球AIS系统中发出。 在这类数据上进行连续轨迹相似性搜索在海上导航和安全等领域有应用价值。 现有的方案通常假设离线环境，并专注于完整轨迹之间的相似性查找。 然而，当应用于在线场景时，这些方案的有效性较低，因为在在线场景中，必须随着新的轨迹数据到达和轨迹演变而不断进行相似性比较。 因此，我们提出了一个针对船舶的实时连续轨迹相似性搜索方法（ACTIVE）。 我们引入了一种新颖的相似性度量——对象轨迹实时距离，该度量强调了船舶预期未来移动趋势，从而实现更具预测性和前瞻性的比较。 接下来，我们提出了一种基于段的船舶轨迹索引结构，将历史轨迹组织成更小且易于管理的部分，以加速相似性计算。 利用这一索引，我们提出了一个高效的连续相似轨迹搜索（CSTS）算法以及多种搜索空间剪枝策略，这些策略在连续相似性搜索过程中减少了不必要的计算，从而进一步提高了效率。 在两个大型真实世界AIS数据集上的大量实验表明， ACTIVE能够显著优于最先进的方法。 ACTIVE显著降低了索引构建成本和索引大小，同时将查询时间减少了70%，命中率提高了60%。 显示英文摘要

摘要： 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.