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The article presents a novel spatio‑temporal trajectory risk metric and an efficient query framework, including a space‑first index (SFT) and distributed storage, to identify high‑risk contacts of COVID‑19 patients from massive movement data, with real‑world deployment results in several Chinese cities.
This article presents a novel method for real-time reachable area analysis that leverages recent trajectory data, introduces a Skip Graph Index for efficient query processing, predicts optimal trajectory‑splicing parameters with machine learning, and demonstrates its effectiveness through extensive experiments on multiple real‑world datasets.
This article presents the CITT framework, a three‑stage algorithm that automatically calibrates road‑intersection topology using massive GPS trajectory data, detailing preprocessing, core‑area detection via quadtree‑mean‑shift, influence‑zone calibration with direction‑weighted Frechet distance and DBSCAN, and demonstrating superior accuracy over existing methods.