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随着移动设备和传感器技术的快速发展，轨迹大数据已成为空间数据智能研究的关键数据源之一。该领域的研究涵盖多源轨迹数据的获取、融合、分析以及模式挖掘与知识发现的完整流程，在智慧城市、交通管理和位置服务等方面展现出巨大潜力。然而，轨迹数据的复杂性和多样性带来了处理、分析和利用方面的诸多挑战。对轨迹数据的获取与预处理、数据存储、模式识别、预测分析等核心方法进行了系统讨论，总结了其在各类应用场景中的最新进展。探讨了当前研究中存在的主要挑战，对未来的研究方向进行展望，为相关领域提供有价值的参考。
Abstract：With the rapid development of mobile devices and sensor technology, trajectory big data has become one of the key data sources in spatial data intelligence research. Research in this field covers the complete process of acquisition, integration, analysis, as well as pattern mining and knowledge discovery of multi-source trajectory data, showing great potential in areas such as smart cities, traffic management, and location-based services. However, the complexity and diversity of trajectory data present numerous challenges in terms of processing, analysis, and utilization. To address these issues, the core methods for trajectory data acquisition and preprocessing, data storage, pattern recognition, and predictive analysis are systematically discussed, and the latest advancements in various application scenarios are summarized. In addition, the main challenges in current research are discussed, and future research directions are outlined, providing a valuable reference for related fields.
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基本信息:
DOI：
中图分类号:TP311.13
引用信息:
[1]李任杰,韩楠,李庆等.空间数据智能中的轨迹大数据分析：多源融合与前沿进展[J].无线电工程,2024,54(12):2735-2743.
基金信息:
国家自然科学基金(62272066); 四川省科技计划(2023YFG0027,2024YFFK0413); 教育部人文社会科学研究规划基金(22YJAZH088); 成都市技术创新研发项目重点项目(2024-YF08-00029-GX);成都市技术创新研发项目(2024-YF05-01217-SN); 成都市区域科技创新合作项目(2023-YF11-00020-HZ); CCF-蚂蚁科研基金项目(CCF-AFSG RF20240106)~~