基于出租车轨迹数据的空间关联规则挖掘  

Spatial Association Rules Mining Based on Taxi Trajectory Data

作　　者：姜晶莉 郭黎[1] JIANG Jingli;GUO Li(Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学

出　　处：《测绘与空间地理信息》2019年第7期56-59,64,共5页Geomatics & Spatial Information Technology

基　　金：国家自然科学基金项目(41471314,41001313)资助

摘　　要：出租车作为城市公共交通的重要组成部分,对于人们的日常活动有着重要的作用。而出租车的运营产生了大量的轨迹数据,通过对轨迹数据的挖掘可以反映城市居民的人口流动状况及出租车的运营规律。空间关联规则挖掘作为数据挖掘的重要组成部分,通过对轨迹数据进行关联规则的挖掘可以得到其隐含的规律信息,从而改进出租车运营模式。而OpenStreetMap是众源地理数据中极具代表性的项目,其数据量丰富、现势性强、成本低廉,被广泛关注。以深圳市出租车轨迹数据及OpenStreetMap矢量地图数据为基本数据,基于出租车上(下)车点进行关联规则挖掘,进而得到深圳人口流动特征,从而为人口活动分析及基于位置的服务提供参考。As an important part of public transportation,taxi plays an significant role in people's daily activities.And the operation of taxi have brought a large amount of trajectory data,it can reflects the situation of population mobility of urban inhabitants as well as the operation pattern of taxi through trajectory data mining.Spatial association rules mining is a major part of data mining,the hidden orderliness and information of trajectory data can be obtained through the association rules mining of which,and thereby the operation mode of taxi can be improved.As a typical project of crowdsource geographic data,OpenStreetMap have attracted many researches gradually for its rich amount of data,high currency and low prices.Taken taxi trajectory data and OpenStreetMap data of Shenzhen Province as basic data,mining spatial association rules between taxi boarding points and breakout points,and then get the characteristic of population flowing of Shenzhen Province,and accordingly provide references for the analysis of people's activities as well as location-based services.

关 键 词：出租车轨迹数据 空间关联规则 数据挖掘 OpenStreetMap 上(下)车点 

分 类 号：P208[天文地球—地图制图学与地理信息工程]