对于庞大的公交地铁路线信息的数据挖掘，一般软件遇到的问题主要有两点：1.对于文本信息的挖掘，特别是中文词汇的挖掘，缺乏成熟的工具或者软件包，2.对于大数据量，一般软件的读取和处理会遇到问题。即使一个月的部分区域路线信息也会达到几百m以上，因此，对于这类数据，无论从算法运行还是数据读取来说普通的SQL语言或者matlab软件处理起来都乏善可陈。对于这类数据，我们一般用r软件可以轻松实现读取，数据挖掘以及可视化的过程。
例如对于下面这样的车站数据：
和近600M的进出站信息的数据, 如果要实现每隔一段时间的对应路线的进出站人数整理以及可视化的过程，我们可以进行一下的步骤进行分析：
首先我们进行数据的读取和预处理
代码语言：javascript复制
install.packages("dplyr")
library("dplyr")#读取dplyr包用以排序
###对数据读取
data=read.table("E:\201501一卡通进出站.txt",stringsAsFactors=F)
##对数据列进行命名
colnames(data)=c("逻辑卡号",
"交易日期" ,
"交易时间",
"票种",
"交易代码",
"交易车站",
"上次交易车站")
###对数据进行预处理
for( ii in 20150101:20150131){#每天的数据
data1=data[which(data[,2]==ii),]#筛选出日期为20150101这天的数据
data2=data1[,c(2,3,6,7)]#筛选出"交易日期" ,"交易时间", "交易车站","上次交易车站"的数据
data2#查看数据
data2=data2[order(data2$交易车站),]
line1=data2[substr(data2$交易车站,1,1)=="1",]#1号线
line2=data2[substr(data2$交易车站,1,1)=="2",]#2号线
###筛选出车站为243
bus=unique(data2[,3])####################每个站的数据
for(busi in 1:length(bus)){
index=which(data2[,3]==bus[busi])#筛选出车站为243的数据行号
data3=data2[index,]#获取交易车站为243的数据
###data3=data2[order(data2$交易车站)，]#如果不筛选车站，直接按交易车站递增排序
data4=arrange(data3,交易日期,交易时间)#对时间排序，先按年份递增排序，然后按照时间递增排序
###按每十分钟时间分割
for (time in 6:21){
for(i in 1:6){
index=intersect(which(data4[,2]>time*10000 (i-1)*1000),which(data4[,2]<=time*10000 1000*i))
datat=data4[index,]
outnum=length(which(datat[,4]!=0))
innum=length(which(datat[,4]==0))
if(i!=6)cat(file=paste("E:\",bus[busi],"车站",ii,"日一卡通进出站时间.txt"),append=TRUE,ii,"日",time,"点",i-1,"0分到",i,"0分的出站人数为",outnum," ","进站人数为",innum,"n")
else cat(file=paste("E:\",bus[busi],"车站",ii,"日一卡通进出站时间.txt"),append=TRUE,ii,"日",time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n")
#cat(file="E:\243车站一卡通进出站时间.txt",append=TRUE,time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n")
}
}
#筛选出出站人数
dataout=data3[which(data3[,4]!=0),]#上次交易车站不为0，为出站人数
datain=data3[which(data3[,4]==0),]
###将数据进行输出
write.table(data4,paste("E:\",ii,"日 ",bus[busi],"车站一卡通进出站整理.txt"))#将数据整理好输出到指定的目录文件名
}
}
####################################################################################3
################1,2号线##########
data2=data2[order(data2$交易车站),]
line1=data2[substr(data2$交易车站,1,1)=="1",]#1号线
line2=data2[substr(data2$交易车站,1,1)=="2",]#2号线
#########1号线
data4=arrange(line1,交易日期,交易时间)#对时间排序，先按年份递增排序，然后按照时间递增排序
###按每十分钟时间分割
cat(file="E:\1号线一卡通进出站时间.txt",append=TRUE, " 点", " 分"," 出站人数", " ","进站人数 " ,"n")
for (time in 6:21){
for(i in 1:6){
index=intersect(which(data4[,2]>time*10000 (i-1)*1000),which(data4[,2]<=time*10000 1000*i))
datat=data4[index,]
outnum=length(which(datat[,4]!=0))
innum=length(which(datat[,4]==0))
if(i!=6)cat(file="E:\1号线一卡通进出站时间.txt",append=TRUE,time," ",i-1,"0 "," ",outnum," "," ",innum,"n")#cat(time,"点",i-1,"0分到",i,"0分的出站人数为",outnum," ","进站人数为",innum,"n")
else cat(file="E:\1号线一卡通进出站时间.txt",append=TRUE,time," ",i-1,"0 "," ",outnum," "," ",innum,"n")#cat(time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n") #
#cat(file="E:\20150101日243车站一卡通进出站时间.txt",append=TRUE,time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n")
}
}
#筛选出出站人数
dataout=data3[which(data3[,4]!=0),]#上次交易车站不为0，为出站人数
datain=data3[which(data3[,4]==0),]
numout=dim(dataout)[1]#出站人数总和
numin=dim(datain)[1]#进站人数总和
###将数据进行输出
write.table(data4,"E:\1号线一卡通进出站整理.txt")#将数据整理好输出到指定的目录文件名
########2号线
data4=arrange(line2,交易日期,交易时间)#对时间排序，先按年份递增排序，然后按照时间递增排序
###按每十分钟时间分割
cat(file="E:\2号线一卡通进出站时间.txt",append=TRUE, " 点", " 分"," 出站人数", " ","进站人数 " ,"n")
for (time in 6:21){
for(i in 1:6){
index=intersect(which(data4[,2]>time*10000 (i-1)*1000),which(data4[,2]<=time*10000 1000*i))
datat=data4[index,]
outnum=length(which(datat[,4]!=0))
innum=length(which(datat[,4]==0))
if(i!=6)cat(file="E:\2号线一卡通进出站时间.txt",append=TRUE,time," ",i-1,"0 "," ",outnum," "," ",innum,"n")#cat(time,"点",i-1,"0分到",i,"0分的出站人数为",outnum," ","进站人数为",innum,"n")
else cat(file="E:\2号线一卡通进出站时间.txt",append=TRUE,time," ",i-1,"0 ", " ",outnum," "," ",innum,"n")#cat(time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n") #
#cat(file="E:\TB related\Service\temp\20150101日243车站一卡通进出站时间.txt",append=TRUE,time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n")
}
}
#筛选出出站人数
dataout=data3[which(data3[,4]!=0),]#上次交易车站不为0，为出站人数
datain=data3[which(data3[,4]==0),]
###将数据进行输出
write.table(data4,"E:\2号线一卡通进出站整理.txt")#将数据整理好输出到指定的目录文件名
#########1,2总和
data4=arrange(line1,交易日期,交易时间)#对时间排序，先按年份递增排序，然后按照时间递增排序
data44=arrange(line2,交易日期,交易时间)#对时间排序，先按年份递增排序，然后按照时间递增排序
cat(file="E:\1,2号线一卡通进出站时间.txt",append=TRUE, " 点", " 分"," 出站人数", " ","进站人数 " ,"n")
for (time in 6:21){
for(i in 1:6){
index=intersect(which(data4[,2]>time*10000 (i-1)*1000),which(data4[,2]<=time*10000 1000*i))
index2=intersect(which(data44[,2]>time*10000 (i-1)*1000),which(data44[,2]<=time*10000 1000*i))
datat=data4[index,]
datat1=data44[index2,]
outnum=length(which(datat[,4]!=0))
outnum1=length(which(datat1[,4]!=0))
innum=length(which(datat[,4]==0))
innum1=length(which(datat1[,4]==0))
if(i!=6)cat(file="E:\1,2号线一卡通进出站时间.txt",append=TRUE,time," ",i-1,"0 "," ",outnum outnum1," "," ",innum innum1,"n")#cat(time,"点",i-1,"0分到",i,"0分的出站人数为",outnum," ","进站人数为",innum,"n")
else cat(file="E:\1,2号线一卡通进出站时间.txt",append=TRUE,time," ",i-1,"0 ", " ",outnum outnum1," "," ",innum innum1,"n")#cat(time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n") #
#cat(file="E:\20150101日243车站一卡通进出站时间.txt",append=TRUE,time,"点",i-1,"0分到",time 1,"点0分的出站人数为",outnum," ","进站人数为",innum,"n")
}
}
}通过以上过程，我们可以将整理后的数据输出到对应的文件中：
以及交通路线的可视化过程；
对于交通路线的网络图来说，r中igraph包的确是实现利器：
代码语言：javascript复制
#读取数据
ljhdat1=readLines("E:/ shanghai_1.txt" )
ljhdat2=readLines("E:/ shanghai_2.txt")
ljhdat3=readLines("E:/ shanghai_3.txt")
ljhdat4=readLines("E:/ shanghai_4.txt")
ljhdat5=readLines("E:/ shanghai_5.txt")
bus=""#建立巴士信息库
for(i in 1:length(ljhdat1)){
if(ljhdat1[i]=="")bus=c(bus,ljhdat1[i-1])#提取每个巴士的路线信息
}
for(i in 1:length(ljhdat2)){
if(ljhdat2[i]=="")bus=c(bus,ljhdat2[i-1])#提取每个巴士的路线信息
}
for(i in 1:length(ljhdat3)){
if(ljhdat3[i]=="")bus=c(bus,ljhdat3[i-1])#提取每个巴士的路线信息
}
for(i in 1:length(ljhdat4)){
if(ljhdat4[i]=="")bus=c(bus,ljhdat4[i-1])#提取每个巴士的路线信息
}
for(i in 1:length(ljhdat5)){
if(ljhdat5[i]=="")bus=c(bus,ljhdat5[i-1])#提取每个巴士的路线信息
}
bus;
bus=bus[-1]
route=list(0)#建立路线信息
#######################分割路线得到站点信息 #################################
route[[1]]=unlist(strsplit(bus[1],split=" "))[-1]
route[[1]]=route[[1]][-which(route[[1]]=="#")]#删除#号
n=length(route[[1]])
library(igraph)
d = data.frame(route[[1]][1:n-1] ,route[[1]][2:n ]#建立邻接矩阵
)
g = graph.data.frame(d, directed = TRUE)
plot(g )
################################分割所有路线得到站点信息###########################
library(igraph)
route1=character(0);
对于最后生成的网络图由于路线众多，在查看的过程中可以通过设置可视化参数来进一步优化。
