摘要
为了探索天气与地铁客流量之间的关系,为地铁运营部门科学合理的调度、预案的制定提供帮助,对地铁大数据进行了关联规则挖掘,并对经典的关联规则算法Apriori进行了改进。改进算法提高了从海量数据中取得频繁项目集的效率,降低了对计算机资源的消耗,高效地挖掘出了天气因素对地铁客流影响的规律。
In order to explore the relationship between weather and subway passenger flow,provide help for scientific and reasonable scheduling and plan formulation.The association rule mining to the subway big data is carried out and the classical association rule algorithm Apriori is improved.The improved algorithm improves the efficiency of obtaining frequent item sets from massive data and reduces the consumption of computer resources,so that the rules of the influence of weather factors on subway passenger flow are mined efficiently.
作者
周永强
杨振华
Zhou Yongqiang;Yang Zhenhua(School of Information Engineering,Xi'An University,Xi'an,Shaanxi 710065,China)
出处
《计算机时代》
2021年第4期57-59,共3页
Computer Era
基金
国家级大学生创新训练项目“智能地铁及地铁大数据分析”。
