摘要
按照自动售取票旅客特征分别提供服务是提高铁路自动售取票工作效率的有效手段。为此,车站需要在不同的区域安装不同类型的自动售取票终端,并配置不同的操作界面。本文在借鉴互联网用户行为分析技术的基础上,提出了一种适应于铁路自动售取票旅客特征分析技术,包括KMeans聚类分析和Top N分析。应用这些分析技术能够为车站提供自动售取票旅客的聚类特征和最常用的售取票方式,从而可以指导车站进行自动售票终端的布局和终端软件界面配置。
Providing different services for different ticket vending machine(TVM) users according to their features is an effective way to increase the TVMs' ticketing efficiency. Therefore, TVMs should be installed at suitable location according to their type and be configured with suitable human machine interface(HMI). Based on the Internet user behavior analysis technologies, some new user feature analysis methods were proposed for railway auto-ticketing system, including KMeans cluster analysis and TopN analysis. By the applications of these analysis technologies, the TVM users' cluster features and the TVM operation modes could be drawn out to improve the TVMs' location layout and the TVMs' HMI configuration.
作者
郭畅
GUO Chang(Guangzhou South Railway Station, Guangzhou Railway (Group) Corporation, Guangzhou 511400, China)
出处
《铁路计算机应用》
2017年第1期36-39,共4页
Railway Computer Application
