Daily Fresh built a custom address database and advanced LBS system—including location correction, trajectory denoising, and stay‑point analysis—to accurately detect and curb fake delivery sign‑offs, achieving over 96% detection accuracy and industry‑leading results.
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.
This article reviews map‑matching challenges caused by GPS errors, categorizes existing algorithms, describes the ST‑Matching approach used on Washington State road data, and outlines key implementation techniques such as projection handling, memory‑pool loading, A* shortest‑path search, and localized indexing to improve accuracy and performance.
This article reviews map‑matching challenges, surveys algorithm categories, describes the ST‑Matching method and its implementation details, and presents experimental results using a large road network and GPS data to achieve high‑accuracy trajectory alignment.
