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时空轨迹预测在交通管理和城市规划中至关重要，但传统模型受数据稀疏性、噪声污染和非线性关系的限制，群体智能驱动的时空轨迹预测技术能够克服传统模型的不足，实现高精度、实时性的预测。对当前群体智能技术研究中常用的群体数据源进行综述，介绍了群体智能的核心优化算法，如粒子群优化和蚁群优化，同时介绍了时空轨迹的表示方式；总结基于概率和基于机器学习的时空轨迹预测方法，概述了群体智能驱动的轨迹预测技术路线；讨论群体智能在当前时空轨迹预测领域的主要应用场景，包括交通路径规划、自然环境监测与操作风险预警等，展望群体智能驱动下的时空轨迹预测技术在认知增强、自治系统和去中心化学习等领域的应用潜力。
Abstract：Spatio-temporal trajectory prediction is essential in traffic management and urban planning, but traditional models are constrained by data sparsity, noise pollution, and non-linear relationships. Swarm intelligence-driven spatio-temporal trajectory prediction technology can overcome the limitations of traditional models, achieving high-precision and real-time prediction. Firstly, the commonly-used swarm data sources in current swarm intelligence research are reviewed, and the core optimization algorithms, such as particle swarm optimization and ant colony optimization are summarized, and the representation of spatio-temporal trajectories is given. Secondly, the probability-based and machine learning-based spatio-temporal trajectory prediction methods are summarized, and the technical route of swarm intelligence-driven trajectory prediction is outlined. Lastly, the main application scenarios of swarm intelligence in current spatio-temporal trajectory prediction are discussed, including traffic path planning, natural environment monitoring, and operational risk warning, and the potential of swarm intelligence-driven spatio-temporal trajectory prediction technology in cognitive enhancement, autonomous systems, and decentralized learning is explored.
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基本信息:
DOI：
中图分类号:U495
引用信息:
[1]潘乐盈,韩楠,罗娜等.群体智能驱动的时空轨迹预测技术综述[J].无线电工程,2024,54(12):2744-2753.
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); 网络空间安全教育部重点实验室开放基金课题(KLCS20240106)~~