摘要
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.
维普
万方数据