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<p>With the widespread adoption of GPS-enabled vehicles, an unprecedented volume of transition trajectory data has become available from various transportation services, including Uber, DiDi ride-sharing platforms, and public transport authorities. These trajectories present significant opportunities for query processing in public transportation planning. In this paper, we investigate two novel classes of coverage queries for transition trajectories: tight coverage query (TCQ) and loose coverage query (LCQ). We further extend these queries by incorporating temporal considerations, introducing the time-constrained coverage query. To efficiently process these queries, we propose the AT-tree, an adaptive index structure that incrementally constructs an index by leveraging previous query results. We also develop some variants: the LAT-tree, TAT-tree and time-constrained AT-tree, which are optimized for LCQ, TCQ and time-constrained coverage query respectively. Additionally, we propose several enhancement strategies to further improve the efficiency of our index structures. Experimental study with real datasets confirms the efficiency and practicality of our index and algorithms.</p>

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Adaptive indexing for coverage queries over transition trajectories

  • 期刊论文

  • 发表日期：

    2025年8月13日

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  • Jian Chen,
  • Junle Chen,
  • Donghua Yang,
  • Guangyu Sui,
  • Jinbao Wang,
  • Lina Chen

摘要

With the widespread adoption of GPS-enabled vehicles, an unprecedented volume of transition trajectory data has become available from various transportation services, including Uber, DiDi ride-sharing platforms, and public transport authorities. These trajectories present significant opportunities for query processing in public transportation planning. In this paper, we investigate two novel classes of coverage queries for transition trajectories: tight coverage query (TCQ) and loose coverage query (LCQ). We further extend these queries by incorporating temporal considerations, introducing the time-constrained coverage query. To efficiently process these queries, we propose the AT-tree, an adaptive index structure that incrementally constructs an index by leveraging previous query results. We also develop some variants: the LAT-tree, TAT-tree and time-constrained AT-tree, which are optimized for LCQ, TCQ and time-constrained coverage query respectively. Additionally, we propose several enhancement strategies to further improve the efficiency of our index structures. Experimental study with real datasets confirms the efficiency and practicality of our index and algorithms.