江西科学2025，Vol.43Issue(2)：385-390,6.DOI:10.13990/j.issn1001-3679.2025.02.024
出租车轨迹的同位模式挖掘算法
Co-location Pattern Mining Algorithms for Taxi Trajectories Data
摘要
Abstract
Taxi trajectory data exhibits a certain degree of complexity,and the existing spatial co-location pattern mining algorithms are not suitable for trajectory datasets.This paper proposes a co-location pattern mining algorithm that considers the spatiotemporal relationships and geometric structures of trajectories.First,the trajectory data structure is stored in an OD(Origin-Destination)data stream structure.Second,the temporal and spatial proximity and directional similarity of trajectories are combined with the computation of spatiotemporal neighborhoods.Finally,the co-location pattern frequency of trajectories is calculated using a taxi trajectory dataset from Nanchang city,covering the period from September 6 to 12,2021.The algorithm is used to identify travel patterns between Nanchang Station,Nanchang West Station and Changbei International Airport at different times of the day.The results show that the method can accurately identify five global spatiotemporal co-location patterns among taxi drivers traveling between these three locations for any period,and effectively analyze three travel patterns across all time periods.关键词
出租车轨迹数据/OD数据流/时空同位模式Key words
taxi trajectory data/OD data streams/spatial and temporal co-location patterns分类
信息技术与安全科学引用本文复制引用
徐诗奕,吴静,傅优杰..出租车轨迹的同位模式挖掘算法[J].江西科学,2025,43(2):385-390,6.基金项目
国家自然科学基金项目(41601416). （41601416）
