南京大学学报(自然科学版) ›› 2018, Vol. 54 ›› Issue (6): 11711182.doi: 10.13232/j.cnki.jnju.2018.06.013
周星星1,2,3,张海平2,3,吉根林1,2,3*
Zhou Xingxing1,2,3,Zhang Haiping2,3,Ji Genlin1,2,3*
摘要: 丰富的居民出行行为信息对挖掘城市热点区域以及居民出行模式有很大的帮助,并且对更好地满足居民出行需求也有一定的启示作用. 最新的相关研究主要聚焦于城市中区域之间的空间移动模式,但并不能识别移动模式发生的时间以及持续的时长. 针对这一问题,提出具有时空特性的区域移动模式挖掘算法STMPZ(Spatio-Temporal based Movement Patterns between Zones). 该算法在DBSCAN(Density-based Spatial Clustering of Applications with Noise)算法的基础上,通过将对象从点扩展成一条出行OD(Origin-Destination)记录,并引入时间特性,最终可以挖掘出具有时空特性的区域移动模式. 为了验证所提出算法的可行性和有效性,利用真实的上海地铁通勤数据集进行实验,实验结果表明,该算法可以快速有效地检测出具有高覆盖率和准确率的区域移动模式. 此外,该算法也可以通过修改聚类过程的参数应用于其他区域或类型的交通数据.
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